The DOI matches its Crossref record; 64 of the 72 references it cites were found in a registry or an index; none of the 21 sources its claims cite is checked for support yet.
Can AI Help in Screening Viral and COVID-19 Pneumonia?
Open accessAbstract
Coronavirus disease (COVID-19) is a pandemic disease, which has already caused thousands of causalities and infected several millions of people worldwide. Any technological tool enabling rapid screening of the COVID-19 infection with high accuracy can be crucially helpful to the healthcare professionals. The main clinical tool currently in use for the diagnosis of COVID-19 is the Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires specialized medical personnel. X-ray imaging is an easily accessible tool that can be an excellent alternative in the COVID-19 diagnosis. This research was taken to investigate the utility of artificial intelligence (AI) in the rapid and accurate detection of COVID-19 from chest X-ray images. The aim of this paper is to propose a robust technique for automatic detection of COVID-19 pneumonia from digital chest X-ray images applying pre-trained deep-learning algorithms while maximizing the detection accuracy. A public database was created by the authors combining several public databases and also by collecting images from recently published articles. The database contains a mixture of 423 COVID-19, 1485 viral pneumonia, and 1579 normal chest X-ray images. Transfer learning technique was used with the help of image augmentation to train and validate several pre-trained deep Convolutional Neural Networks (CNNs). The networks were trained to classify two different schemes: i) normal and COVID-19 pneumonia; ii) normal, viral and COVID-19 pneumonia with and without image augmentation. The classification accuracy, precision, sensitivity, and specificity for both the schemes were 99.7%, 99.7%, 99.7% and 99.55% and 97.9%, 97.95%, 97.9%, and 98.8%, respectively. The high accuracy of this computer-aided diagnostic tool can significantly improve the speed and accuracy of COVID-19 diagnosis. This would be extremely useful in this pandemic where disease burden and need for preventive measures are at odds with available resources.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This paper proposes transfer learning with pre-trained CNNs to detect COVID-19 pneumonia from chest X-ray images, using a newly assembled public database of 423 COVID-19, 1,485 viral pneumonia, and 1,579 normal images. It reports very high classification performance, especially for DenseNet201 with image augmentation in the three-class task.
Key findings
- For two-class classification (normal vs. COVID-19 pneumonia), the reported accuracy, precision, sensitivity, and specificity were 99.7%, 99.7%, 99.7%, and 99.55%, respectively.
- For three-class classification (normal, viral pneumonia, and COVID-19 pneumonia), the reported accuracy, precision, sensitivity, and specificity were 97.9%, 97.95%, 97.9%, and 98.8%, respectively.
- CheXNet achieved the highest two-class accuracy of 99.4% without augmentation and 99.7% with augmentation; DenseNet201 outperformed other models in the three-class scheme when image augmentation was used.
- In the DenseNet201 confusion matrix with augmentation, only 3 of 423 COVID-19 images were misclassified as normal in the two-class problem, and only 1 COVID-19 image was misclassified as normal in the three-class problem.
Methods
- The authors created a public database by combining several public sources and collecting images from published articles: 423 COVID-19, 1,485 viral pneumonia, and 1,579 normal chest X-ray images.
- Eight pre-trained CNNs were trained, validated, and tested using transfer learning: MobileNetv2, SqueezeNet, ResNet18, ResNet101, DenseNet201, CheXNet, Inceptionv3, and VGG19.
- Two classification schemes were evaluated with and without image augmentation: two-class (normal vs. COVID-19) and three-class (normal, viral pneumonia, and COVID-19), using stratified 5-fold cross-validation with 80% training and 20% test splits.
Limitations
- The paper does not include a dedicated limitations section; it states that three COVID-19 images were misclassified and that cases without evident light-focus edge features in deep layers “have to be confirmed by other techniques.”
- The paper notes that results reported on smaller databases in the literature should be evaluated on a large dataset to be generalizable.
- Evident from the text: the study uses a retrospectively assembled public/image-article dataset rather than a prospective clinical cohort, and no external validation cohort or full reader study is described; only three misclassified images were evaluated by three practicing radiologists.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
0 of 21 cited sources checked · 0 supported · 0 flagged
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“COVID-19 sub-database, comprising of 423 AP/PA images, was created from the following four major data sources.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > A. DATABASE DESCRIPTION
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Images from RSNA-Pneumonia-Detection-Challenge database along with the Chest X-ray Images database from Kaggle were used to create the normal and viral pneumonia sub-databases of 1579 and 1485 X-ray images respectively.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > A. DATABASE DESCRIPTION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is apparent from Table 2 that all the evaluated pre-trained models perform very well in classifying COVID-19 and normal images in two-class problem.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > A. EXPERIMENTAL RESULTS-TWO CLASS PROBLEM
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“On the other hand, while the image augmentation was applied to the training image set, all the pre-trained networks are now performing based on their capability to distinguish the three-class images.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > B. EXPERIMENTAL RESULTS-THREE CLASS PROBLEM
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is notable from Figure 8 that the 14 th layer of the DenseNet201 can detect features that can distinguish normal, COVID-19 and Viral Pneumonia images.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > B. EXPERIMENTAL RESULTS-THREE CLASS PROBLEM
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, the high precision and F1 score show that the network is still performing excellent in classifying most of the images reliably.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > B. EXPERIMENTAL RESULTS-THREE CLASS PROBLEM
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The difference between normal and COVID-19 X-ray images can be observed in the deep convolutional layer of pre-trained CNN model.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > B. EXPERIMENTAL RESULTS-THREE CLASS PROBLEM
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Interestingly, CheXNet which is a 121-layer variant of DenseNet trained on X-ray images, is not outperforming a deeper variant of DenseNet with 201-layers.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > B. EXPERIMENTAL RESULTS-THREE CLASS PROBLEM
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, CheXNet is still performing well while trained on a small dataset as CheXNet was originally trained one a very large X-ray image dataset.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > B. EXPERIMENTAL RESULTS-THREE CLASS PROBLEM
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Two other COVID-19 images were miss-classified as viral pneumonia images.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > B. EXPERIMENTAL RESULTS-THREE CLASS PROBLEM
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
- It may state two separate claims as one. 34
- The quote only partly states this claim. 1
- Probably not a substantial claim. 11
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The authors in this paper have prepared a large database of X-ray images of 1579 normal, 1485 viral pneumonia and 423 COVID-19 positive pneumonia and made this publicly available so that other researchers can get benefit from it.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > I. INTRODUCTION
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Authors managed to collect 60 COVID-19 positive chest X-ray images from 43 recently published articles [60], which were not listed in the GitHub database and 32 positive chest x-ray images from Radiopaedia [63], which were not listed in the GitHub database.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > A. DATABASE DESCRIPTION
What the sources say
- Not checked yetcites [60] source
- Not checked yetcites [63]
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“CheXNet is producing the highest accuracy of 99.4% and 99.7% for two-class classification without and with image augmentation respectively.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > A. EXPERIMENTAL RESULTS-TWO CLASS PROBLEM
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Among the networks trained with 338 X-ray images for two-class problem, ResNet18 and CheXNet are equally performing for classifying images while CheXNet and DenseNet201 are performing better than others in case of training with augmented images, although the difference is marginal.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > A. EXPERIMENTAL RESULTS-TWO CLASS PROBLEM
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The weighted average performance matrix for eight different networks are very similar whereas small gain can be observed when training was done using image augmentation.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > A. EXPERIMENTAL RESULTS-TWO CLASS PROBLEM
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It was noticed that the strongest channel activates on edges with positive activation on light left/dark right edges, and negative activation on dark left/light right edges.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > E. INVESTIGATION OF THE DEEP LAYER FEATURES
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is clear from Figure 7(A) that only three COVID-19 images out of 423 images were miss-classified to normal (false negative) and only three images out of 1579 images were miss-classified to COVID-19 (false positive).”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > B. EXPERIMENTAL RESULTS-THREE CLASS PROBLEM
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“None of normal images were miss-classified to COVID-19 by the three-class classifier although several normal images were miss-classified to viral pneumonia.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > B. EXPERIMENTAL RESULTS-THREE CLASS PROBLEM
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the three-class problem, only one COVID-19 image was miss-classified to normal, which is one of the three images miss-classified by the two-class classifier.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > B. EXPERIMENTAL RESULTS-THREE CLASS PROBLEM
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“If the pre-trained networks are trained on a small image dataset as reported by the most of the research groups in the literature, the performance difference is very marginal and overall performance is reduced for three-class problem in comparison to two-class problem.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > B. EXPERIMENTAL RESULTS-THREE CLASS PROBLEM
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It was observed that DenseNet201 outperforms other different deep CNN networks while image augmentation was used for training the CNN models.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > IV. CONCLUSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Only four viral pneumonia images were miss-classified to COVID-19 out of 1485 images while 33 images miss-classified to normal.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > B. EXPERIMENTAL RESULTS-THREE CLASS PROBLEM
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The classification accuracy, precision, sensitivity, and specificity for both the schemes were 99.7%, 99.7%, 99.7% and 99.55% and 97.9%, 97.95%, 97.9%, and 98.8%, respectively.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The classification accuracy, precision, sensitivity, and specificity for both the schemes were 99.7%, 99.7%, 99.7% and 99.55% and 97.9%, 97.95%, 97.9%, and 98.8%, respectively.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“First and third images were identified as no sign or very little sign of COVID-19 by the radiologists while image-02 was identified as very mild stage of lung infections.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > B. EXPERIMENTAL RESULTS-THREE CLASS PROBLEM
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“CheXNet which is a variant of DenseNet was outperforming other networks while image augmentation was not used.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > IV. CONCLUSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The classification accuracy, precision, sensitivity, and specificity of normal and COVID-19 images, and normal, COVID-19 and viral pneumonia were (99.7%, 99.7%, 99.7% and 99.55%), and (97.9%, 97.95%, 97.9%, and 98.8%) respectively.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > IV. CONCLUSION
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The classification accuracy, precision, sensitivity, and specificity of normal and COVID-19 images, and normal, COVID-19 and viral pneumonia were (99.7%, 99.7%, 99.7% and 99.55%), and (97.9%, 97.95%, 97.9%, and 98.8%) respectively.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > IV. CONCLUSION
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“Moreover, there is a significant shortage of its supply, which leads to delay in the disease prevention efforts [6].”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > I. INTRODUCTION
What the sources say
- Not checked yetcites [6] source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“Specifically, Convolutional Neural Network (CNN) has been proven extremely beneficial in feature extraction and learning and therefore, widely adopted by the research community [26].”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > I. INTRODUCTION
What the sources say
- Not checked yetcites [26]
Strength:boosted· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This makes it difficult to generalize their results reported in these articles and cannot guarantee that the reported performance will retain when these models will be tested on a larger dataset.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > I. INTRODUCTION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“RT-PCR is used as a reference method for the detection of COVID-19 patients, however, the technique is manual, complicated, laborious and time-consuming with a positivity rate of only 63% [5].”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > I. INTRODUCTION
What the sources say
- Not checked yetcites [5] source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“A rigorous experiment on a large database of COVID and non-COVID classes are very few and missing in case of transfer learning approach.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > I. INTRODUCTION
What the sources say
- Not checked yetcites [40]- [56]
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“X-ray imaging is an easily accessible tool that can be an excellent alternative in the COVID-19 diagnosis.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“It is reported that the RT-PCR kit costs about USD 120-130 and also requires a specialized biosafety lab to house the PCR machine, each of which may cost USD 15,000 to USD 90,000 [8].”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > I. INTRODUCTION
What the sources say
- Not checked yetcites [8] source
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Image augmentation techniques were applied to viral pneumonia, normal and COVID-19 X-ray images for training to create a balanced training set.However, COVID-19 images were augmented six times while normal and viral pneumonia images were augmented once only.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > C. PREPROCESSING
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“The concept of transfer learning uses the trained model from large dataset such as ImageNet [57] and modify the Softmax and classification layer of the pre-trained networks.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > II. METHODOLOGY
What the sources say
- Not checked yetcites [57] source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Both the experiments were evaluated using a stratified 5-fold cross-validation (CV) scheme with a ratio of 80% for training and 20% for the test (unseen folds) splits, where 10% of training data is used as a validation set to avoid overfitting.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > C. PREPROCESSING
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Fivefold cross-validation result was averaged to produce the final receiver operating characteristic (ROC) curve, confusion matrix, and evaluation matrices.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > B. CNN MODEL SELECTION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“In the study1, image augmentation technique was not applied to the training data.Since COVID-19 positive chest X-ray images were 423, same number of X-ray images were randomly selected from normal (out of 1579) and viral pneumonia (out of 1485) images to match with COVID-19 images to balance the database.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > C. PREPROCESSING
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Interestingly, CheXNet is performing well in both the cases, with and without augmentation and this can be explained from the fact that CheXNet is the only network which is pre-trained on a large.X-ray image database and the network supposed to perform better for X-ray image classification without the requirement of training again on a larger dataset.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > A. EXPERIMENTAL RESULTS-TWO CLASS PROBLEM
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, in this classification problem as the COVID-19 images are significantly different from normal images all the tested networks are performing well.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > A. EXPERIMENTAL RESULTS-TWO CLASS PROBLEM
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: COVID-19 X-ray images are significantly different from normal images.
None of the sources cited for this claim have been checked yet.
In the paper
“This removes the requirement of having large dataset and also reduces the long training period as is required by the deep learning algorithm when developed from scratch [58], [59].”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > II. METHODOLOGY
What the sources say
- Not checked yetcites [58]
- Not checked yetcites [59]
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This would be extremely useful in this pandemic where disease burden and need for preventive measures are at odds with available resources.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > Abstract
Strength:hedged· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Typically, the deeper the network the better is the performance in distinguishing the image classes.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > B. EXPERIMENTAL RESULTS-THREE CLASS PROBLEM
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“This shows the reason of the success of the network in detecting COVID-19 X-ray images and distinguishing it from normal and viral pneumonia images, which several groups of researchers reported earlier are not reliably possible by plain X-ray images [69]- [72].”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > B. EXPERIMENTAL RESULTS-THREE CLASS PROBLEM
What the sources say
- Not checked yetcites [69]- [72]
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, it can be summarized that even though CheXNet was trained originally on X-ray images but training an even deeper network with a larger image set can give better chance of training from the new image sets on which the training is done, i.e., deep network can learn better and perform better if the training is carried out on a larger dataset.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > B. EXPERIMENTAL RESULTS-THREE CLASS PROBLEM
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It can be noted that network is not confusing between COVID-19, and other two image classes rather network is more confused between viral pneumonia and normal images.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > B. EXPERIMENTAL RESULTS-THREE CLASS PROBLEM
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Since a large number of patients attending out-door or emergency, doctor's time is limited and computeraided-diagnosis can save lives by early screening and propercare.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > IV. CONCLUSION
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
- Rests on an assumption: Doctor's time is limited because a large number of patients attend out-door or emergency.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This clearly reveals the fact that the performance reported on smaller database in the literature should be evaluated on a large dataset otherwise, the findings of these studies cannot be generalized for real applications.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > IV. CONCLUSION
Tests✓ Asserted: passes? Checkable: unclear✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The algorithm fails if no evident light focus edge feature is appeared in the deep layer and this type of COVID-19 cases have to be confirmed by other techniques.”
§ Can AI Help in Screening Viral and COVID-19 Pneumonia? > B. EXPERIMENTAL RESULTS-THREE CLASS PROBLEM
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper relies on this without support.
Deep convolutional neural networks typically perform better with a larger dataset than a smaller one.
“Deep convolutional neural networks typically perform better with a larger dataset than a smaller one.”
The paper relies on this without support.
The number of chest X-ray images publicly available online is small and scattered.
“Although there are a large number of COVID-19 patients infected worldwide, the number of chest X-ray images publicly available online are small and scattered.”
The paper relies on this without support.
COVID-19 X-ray images are significantly different from normal images.
“as the COVID-19 images are significantly different from normal images all the tested networks are performing well.”
The paper relies on this without support.
Images of various viral pneumonias are similar and overlap with other infectious and inflammatory lung diseases.
“the images of various viral pneumonias are similar and they overlap with other infectious and inflammatory lung diseases.”
The paper relies on this without support.
The symptoms of COVID-19 are similar to those of viral pneumonia.
“The symptoms of COVID-19 being similar to that of viral pneumonia”
The paper relies on this without support.
CheXNet was originally trained on a very large X-ray image dataset.
“CheXNet was originally trained one a very large X-ray image dataset.”
“CheXNet was pre-trained on a large X-ray database”
The paper relies on this without support.
There is a large degree of variability in the input images from X-ray machines due to variations in the expertise of the radiologist.
“Moreover, there is a large degree of variability in the input images from the X-ray machines due to the variations of expertise of the radiologist.”
The paper relies on this without support.
The evaluations of the three practicing radiologists are reliable for identifying signs of COVID-19 in the miss-classified images.
“These three images were evaluated by three practicing radiologists to identify what is their evaluations for these three images.”
The paper relies on this without support.
Doctor's time is limited because a large number of patients attend out-door or emergency.
“Since a large number of patients attending out-door or emergency, doctor's time is limited”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“For instance, while specialized models like IMISNet demonstrate exceptional proficiency in CT datasets, their performance significantly decreases when applied to X-Ray, as observed in the Radiography (Chowdhury et al., 2020) results.”
From MedSAM-Agent: Empowering Interactive Medical Image Segmentation with Multi-turn Agentic Reinforcement Learning (2026) · cites this paper as Chowdhury et al., 2020
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“As illustrated in Fig. 3, which details the performance trajectories on the FH-PS-AOP (Jieyun & ZhanHong, 2024) and Radiography (Chowdhury et al., 2020) datasets, MedSAM-Agent outperforms these ideal single-turn baselines.”
From MedSAM-Agent: Empowering Interactive Medical Image Segmentation with Multi-turn Agentic Reinforcement Learning (2026) · cites this paper as Chowdhury et al., 2020
In the citing paper
“COVID-19 Chest X-ray (also known as COVID-19 CXR) [109] 9 is a publicly-available database of chest X-ray images, containing 3616 COVID-19 positive cases, 10,192 normal controls, 6012 lung opacity (non-COVID infection), and 1345 viral pneumonia cases.”
From Federated learning for medical image analysis: A survey (2024) · cites this paper as [109]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Citation [51] does not identify the Khan study
MinorThe text cites [51] for Khan et al.'s study, but reference [51] lists Ucar and Korkmaz; the Khan et al. paper is listed at [56].
“Khan et al. [51] applied transfer learning approach on 310 normal, 330 bacterial pneumonia, 327 viral pneumonia and 284 COVID-19 pneumonia images.”
“51. F. Ucar and D. Korkmaz, ''COVIDiagnosis-Net: Deep Bayes-squeezenet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images,'' Med. Hypotheses, vol. 140, Jul. 2020, Art. no. 109761.”
Thread-reader dataset cites an unrelated reference
MinorThe dataset description cites [64] for the Spanish physician's thread-reader images, but reference [64] is a ChestX-ray8 database paper.
“• COVID-19 Chest imaging at thread reader: A physician has shared 103 images for 50 different cases with varying resolution from his hospital in Spain to the Chest imaging at thread reader [64].”
“64. X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, and R. M. Summers, ''ChestX- ray8: hospital-scale chest X-ray database and benchmarks on weakly- supervised classification and localization of common thorax diseases,'' in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jul. 2017, pp. 2097-2106.”
Citation [47] does not identify the Ucar study
MinorThe text cites [47] for Ucar et al.'s SqueezeNet work, but reference [47] is a paper by Zhang et al.; the Ucar and Korkmaz paper is listed at [51].
“Ucar et al. [47] has fine-tuned SqueezeNet pre-trained network with Bayesian optimization to classify COVID-19 images”
“47. J. Zhang, Y. Xie, Z. Liao, G. Pang, J. Verjans, W. Li, Z. Sun, J. He, Y. Li, C. Shen, and Y. Xia, ''Viral pneumonia screening on chest X-ray images using confidence-aware anomaly detection,'' 2020, arXiv:2003.12338.”
COVID-CAPS is attributed to the wrong reference
MinorThe text cites [43] for COVID-CAPS, whereas reference [43] is by Apostolopoulos and Mpesiana; the COVID-CAPS paper appears at [49].
“Ashfar et al. [43] proposed a Capsule Networks, called COVID-CAPS rather than a conventional CNN to deal with a smaller dataset.”
“43. I. D. Apostolopoulos and T. A. Mpesiana, ''Covid-19: Automatic detection from X-ray images utilizing transfer learning with convolutional neural networks,'' Phys. Eng. Sci. Med., vol. 43, no. 2, pp. 635-640, Jun. 2020.”
Citation [39] does not identify the Ioannis study
MinorThe text attributes a COVID-19 classification study to Ioannis et al. at [39], but reference [39] is a paper by Lakhani and Sundaram on tuberculosis.
“Ioannis et al. [39] reported transfer learning approach for classifying dataset of 1427 X-ray images”
“39. P. Lakhani and B. Sundaram, ''Deep learning at chest radiography: Auto- mated classification of pulmonary tuberculosis by using convolutional neural networks,'' Radiology, vol. 284, no. 2, pp. 574-582, Aug. 2017.”
Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., Qiu, Y., Wang, J., Liu, Y., Wei, Y., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30211-7 · Cited by 21,415
Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., Wang, B., Xiang, H., Cheng, Z., Xiong, Y., et al. (2020) · JAMA · doi:10.1001/jama.2020.1585 · Cited by 20,148
La certeza de lo impredecible: Cultura Educación y Sociedad en tiempos de COVID19
Martínez González, M. (2020) · DSPACE System - Metalibrary (University of the Coast) · Cited by 18,463
Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia
Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., Ren, R., Leung, K. S. M., Lau, E. H. Y., Wong, J. Y., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001316 · Cited by 16,575
Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR
Corman, V. M., Landt, O., Kaiser, M., Molenkamp, R., Meijer, A., Chu, D. K. W., Bleicker, T., Brünink, S., Schneider, J. A., Schmidt, M. L., et al. (2020) · Eurosurveillance · doi:10.2807/1560-7917.es.2020.25.3.2000045 · Cited by 7,266
Flagged first, then by citations
8 of the 72 references couldn’t be matched to a known paper.
Research works printed but not linked: 18
- weinstock, 2020, Chest X-ray findings in 636 ambulatory patients with COVID-19 presenting to an urgent care center: A normal chest X-ray is no guarantee, J Urgent Care Med, 14, 8
- gershgorn, 2017, The Data that Transformed AI Research-and Possibly the World
- muhammad, 2020, COVID-19 Chest X-Ray Database
- monteral, 2020, COVID-Chestxray Database
- radiology, 2020, COVID-19 Database
- 2020, Radiopedia
- imaging, 2020, This is a Thread of COVID-19 CXR (All SARS-CoV-2 PCR+) From my Hospital (Spain) I Hope it Could Help
- 10.1109/cvpr.2017.369 · DOI · Paper page
- mooney, 2018, Chest X-Ray Images (Pneumonia)
- 2020, Inception Understanding Various Architectures of Convolutional Networks
- 2020, Coronavirus Disease 2019 (COVID-19
- 2020, WHO Director-General’s Opening Remarks at the Media Briefing on COVID-19–11 March 2020
- medicine, 2020, Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU)
- 2020, Global COVID-19 Report
- news, 2020, Bangladesh Scientists Create 3 Kit Can it Help Detect COVID-19
- news, 2020, India’s Poor Testing Rate May Have Masked Coronavirus Cases
- wetsman, 2020, Coronavirus Testing Shouldn’T Be This Complicated
- joaquin, 2020, Using Deep Learning to Detect Pneumonia Caused by NCOV-19 From X-Ray Images
Cited by
Sorted by citations
Items 901–1,000 of 1,005
Socha, M., Prażuch, W., Suwalska, A., Foszner, P., Tobiasz, J., Jaroszewicz, J., Gruszczyńska, K., Sliwinska, M., Nowak, M., Giżycka, B., et al. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107684 · Cited by 6
From single to multiple: Generalized detection of Covid-19 under limited classes samples
Zheng, K., Wu, J., Yuan, Y., & Liu, L. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107298 · Cited by 6
Sholik, M., Fatichah, C., & Amaliah, B. (2023) · IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) · doi:10.1109/iaict59002.2023.10205826 · Cited by 6
Hallaci, S., Farou, B., Kouahla, Z., & Séridi, H. (2023) · Evolving Systems · doi:10.1007/s12530-023-09541-w · Cited by 6
DB-COVIDNet: A Defense Method against Backdoor Attacks
Shamshiri, S., Han, K. J., & Sohn, I. (2023) · Mathematics · doi:10.3390/math11204236 · Cited by 6
Basha, S. M., de Albuquerque, V. H. C., Chelloug, S. A., Elaziz, M. A., Mohisin, S. H., & Pathan, S. P. (2023) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2023.031425 · Cited by 6
Zhang, H., Lv, Z., Liu, S., Sang, Z., & Zhang, Z. (2024) · Discover Applied Sciences · doi:10.1007/s42452-024-05796-3 · Cited by 6
A Novel Approach to Differentiate COVID-19 Pneumonia in Chest X-ray
de Moura, L. V., Dartora, C. M., Mattjie, C., Barros, R. C., & da Silva, A. M. M. (2020) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe50027.2020.00078 · Cited by 6
Implementation of CNN based COVID-19 classification model from CT images
Kaya, A., Ataş, K., & Myderrizi, I. (2021) · IEEE World Symposium on Applied Machine Intelligence and Informatics (SAMI) · doi:10.1109/sami50585.2021.9378646 · Cited by 6
Enhance Portable Radiograph for Fast and High Accurate COVID-19 Monitoring
Le, N., Sorensen, J. I. L., Bui, T., Choudhary, A. K., Luu, K., & Nguyen, H. V. (2021) · Diagnostics · doi:10.3390/diagnostics11061080 · Cited by 7
COVID-19 Diagnosis Using Deep Learning
Güraksın, G. E., Barın, S., Özgül, E., & Kaya, F. (2021) · Düzce Üniversitesi Bilim ve Teknoloji Dergisi · doi:10.29130/dubited.866124 · Cited by 6
El-Bouzaidi, Y. E. I., & Abdoun, O. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-73882-2_88 · Cited by 6
Local Binary Pattern Based COVID-19 Detection Method Using Chest X-Ray Images
Bozkurt, F., & Bayram, E. (2021) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu53274.2021.9477796 · Cited by 6
Application of deep learning to identify COVID-19 infection in posteroanterior chest X-rays
Maharjan, J., Calvert, J. S., Pellegrini, E., Green‐Saxena, A., Hoffman, J. L., McCoy, A., Mao, Q., & Das, R. (2021) · Clinical Imaging · doi:10.1016/j.clinimag.2021.07.004 · Cited by 6
Law, B. K., & Lin, L. P. (2021) · IEEE International Conference on Signal and Image Processing Applications (ICSIPA) · doi:10.1109/icsipa52582.2021.9576804 · Cited by 6
Automatic Quality Control in Lung X-Ray Imaging with Deep Learning
Довганич, А. А., Khvostikov, A. V., Krylov, A. S., & Parolina, L. (2021) · Computational Mathematics and Modeling · doi:10.1007/s10598-021-09539-6 · Cited by 6
Jasthy, S., Radhakrishna, V., & Dutta, S. R. (2021) · MENDEL · doi:10.13164/mendel.2021.2.080 · Cited by 7
A novel abnormality annotation database for COVID-19 affected frontal lung X-rays
Mittal, S., Venugopal, V. K., Agarwal, V., Malhotra, M., Chatha, J. S., Kapur, S., Gupta, A., Batra, V., Majumdar, P., Malhotra, A., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0271931 · Cited by 6
Yaşar, A. (2023) · Tehnički glasnik · doi:10.31803/tg-20220828220446 · Cited by 6
Harnessing intelligent technologies to curb COVID-19 pandemic: taxonomy and open challenges
Khan, S., Khan, M. K., & Khan, R. (2021) · Computing · doi:10.1007/s00607-021-00983-1 · Cited by 7
Self-citationÖksüz, C., Urhan, O., & Güllü, M. K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.09132 · Cited by 6
Jindal, M., & Singh, B. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110583 · Cited by 6
A review: Lightweight architecture model in deep learning approach for lung disease identification
Maharani, D. A., Utaminingrum, F., Husnina, D. N. N., Sukmaningrum, B., Rahmania, F. N., Handani, F., Chasanah, H. N., Arrahman, A., & Febrianto, F. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110425 · Cited by 5
Detecting SARS-CoV-2 in CT Scans Using Vision Transformer and Graph Neural Network
Amuda, K. A., WAKILI, A. L. M. U. S. T. A. P. H. A. A., Amoo, T., Agbetu, L., Wang, Q., & Feng, J. H. (2025) · Algorithms · doi:10.3390/a18070413 · Cited by 5
Zhou, M., Gao, L., Bian, K., Wang, H., Wang, N., Chen, Y., & Liu, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-13479-1 · Cited by 5
Rahat, M. M. A., Islam, M. I., Miah, M. S. U., & Alharbi, T. (2026) · Scientific Reports · doi:10.1038/s41598-025-33178-1 · Cited by 5
Self-citationHaque, M. R., & Mamun, M. M. A. (2022) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit57492.2022.10054663 · Cited by 5
Selective Kernel Networks for Lung Abnormality Diagnosis Using Chest X-rays
Phogat, D., Parasu, D., Prakash, A., & Sowmya, V. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-5166-6_63 · Cited by 5
Kumar, U. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18670-6 · Cited by 5
Gopatoti, A., Jayakumar, R., Billa, P., & Patteeswaran, V. (2024) · Journal of X-Ray Science and Technology · doi:10.3233/xst-230421 · Cited by 5
Ding, H., Fan, L., Zhang, J., & Gao, G. (2024) · British Journal of Hospital Medicine · doi:10.12968/hmed.2024.0244 · Cited by 5
Privacy-preserving deep learning model with integer quantization and secure multi-party computation
Tran, A.-T., Luong, T. D., & Pham, X. S. (2024) · Annals of Operations Research · doi:10.1007/s10479-024-06151-w · Cited by 5
Noninvasive Detection of Respiratory Disorder Due to COVID-19 at the Early Stages in Saudi Arabia
Boulila, W., Shah, S. A., Ahmad, J. E., Driss, M., Ghandorh, H., Alsaeedi, A., Al-Sarem, M., & Saeed, F. (2021) · Electronics · doi:10.3390/electronics10212701 · Cited by 5
Transfer Learning Based Method for Automatic COVID-19 Cases Detection in Chest X-Ray Images
Sakib, S., Siddique, M. A. B., Khan, M. M. R., Yasmin, N., Aziz, A., Chowdhury, M., & Tasawar, I. K. (2021) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec51865.2021.9591856 · Cited by 5
Self-citationDeep learning classification of Covid-19, pneumonia, and lung cancer on chest radiographs
Falana, W. O., Serener, A., & Serte, S. (2021) · International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) · doi:10.1109/ismsit52890.2021.9604629 · Cited by 5
Multi-Classification Network for Identifying COVID-19 Cases Using Deep Convolutional Neural Networks
Sarker, S., Tan, L., Ma, W., Rong, S., Osibo, B. K., & Darteh, O. F. (2021) · Journal on internet of things · doi:10.32604/jiot.2021.014877 · Cited by 5
Abbasi, W. A., Abbas, S. A., Andleeb, S., Bibi, M., Majeed, F., Jaleel, A., & Akhtar, M. N. (2021) · Quantitative Biology · doi:10.15302/j-qb-021-0278 · Cited by 8
COVID-19 Diagnosis Using VGG-16 with CT Scans
Yadlapalli, P., Kora, P., Kumari, U., Gade, V. S. R., & Padma, T. (2022) · International Conference for Advancement in Technology (ICONAT) · doi:10.1109/iconat53423.2022.9725908 · Cited by 5
Multi-Class Flat Classification of Lung Diseases Utilizing Deep Learning
Gargees, R. S. (2022) · IEEE IAS Global Conference on Emerging Technologies (GlobConET) · doi:10.1109/globconet53749.2022.9872480 · Cited by 5
David, L. R., Elshami, W., Alshuweihi, A., Obaideen, A. K., Issa, B. A., & Shetty, S. R. (2022) · Advances in Biomedical and Health Sciences · doi:10.4103/abhs.abhs_29_22 · Cited by 5
Machine-Learning-Based COVID-19 Detection with Enhanced cGAN Technique Using X-ray Images
Hamdi, M., Ksibi, A., Ayadi, M. F., Elmannai, H., & Alzahrani, A. I. A. (2022) · Electronics · doi:10.3390/electronics11233880 · Cited by 5
Sunyoto, A., Pristyanto, Y., Setyanto, A., Alarfaj, F. K., Almusallam, N., & Alreshoodi, M. A. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0130923 · Cited by 7
Novel Approach in Classification and Prediction of COVID-19 from Radiograph Images using CNN
Kanumuri, C., Madhavi, C. R., & Ravichandra, T. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0130966 · Cited by 5
Roy, S., & Khurana, R. (2023) · ACM Symposium on Applied Computing · doi:10.1145/3555776.3577825 · Cited by 5
Alamsyah, A., Prasetiyo, B., Hakim, M. F. A., & Pradana, F. D. (2023) · AIP conference proceedings · doi:10.1063/5.0125767 · Cited by 5
A novel parameter decoupling approach of personalised federated learning for image analysis
Su, R., Pang, X., & Wang, H. (2023) · IET Computer Vision · doi:10.1049/cvi2.12204 · Cited by 5
Image Enhancement using Recursive Anisotropic and Stationary Wavelet Transform
Elmasry, R. M., Salem, M. A. ‐., Fahmy, O. M., & Ghany, M. A. A. E. (2023) · International Conference on Systems, Signals and Image Processing · doi:10.1109/iwssip58668.2023.10180278 · Cited by 5
Parsarad, S., Saeedizadeh, N., Soufi, G. J., Shafieyoon, S., Hekmatnia, F., Zarei, A. P., Soleimany, S., Yousefi, A., Nazari, H., Torabi, P., et al. (2023) · Journal of Imaging · doi:10.3390/jimaging9080159 · Cited by 4
A Deep Learning Model Based on Capsule Networks for COVID Diagnostics through X-ray Images
Rangel, G., Cuevas-Tello, J. C., Rivera, M., & Renteria, O. (2023) · Diagnostics · doi:10.3390/diagnostics13172858 · Cited by 5
Kaur, J., & Kaur, P. (2023) · New Generation Computing · doi:10.1007/s00354-023-00232-3 · Cited by 5
Wan, Y., Jia, S., Li, Y., Wang, R., Guo, K., Wang, M., & Liu, R. (2023) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2023.109011 · Cited by 5
Federated Learning Using the Particle Swarm Optimization Model for the Early Detection of COVID-19
Kandati, D. R., Srivastava, G., Zhu, Y., Supriya, Y., Yenduri, G., Victor, N., Anusha, S., & Gadekallu, T. R. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-8132-8_32 · Cited by 5
LFC-UNet: learned lossless medical image fast compression with U-Net
Liao, H., & Li, Y. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.1924 · Cited by 5
Patil, P., & Narawade, V. E. (2024) · International Research Journal of Multidisciplinary Technovation · doi:10.54392/irjmt24211 · Cited by 5
Pal, M., Parija, S., & Panda, G. (2024) · e-Prime - Advances in Electrical Engineering Electronics and Energy · doi:10.1016/j.prime.2024.100547 · Cited by 9
Application of Deep Transfer Learning in Medical Imaging for Thyroid Lesion Diagnostic Assistance
Chaouchi, L., Gaceb, D., Touazi, F., Djani, D., & Yakoub, A. (2024) · International Conference on Image and Signal Processing and their Applications (ISPA) · doi:10.1109/ispa59904.2024.10536856 · Cited by 5
Singh, L. K., Khanna, M., Garg, H., & Singh, R. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19549-2 · Cited by 5
Contemporary Study for Detection of COVID-19 Using Machine Learning with Explainable AI
Akbar, S., Azam, H., Almutairi, S. S., Alqahtani, O., Shah, H., & Aleryani, A. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.050913 · Cited by 5
Song, Z., Shi, Z., Yan, X., Zhang, B., Song, S., & Tang, C. C. (2024) · Electronics · doi:10.3390/electronics13152929 · Cited by 10
Interpretable Deep Learning Model for Tuberculosis Detection Using X-Ray Images
Ahamed, M. F., Nahiduzzaman, M., Islam, M. R., Rahman, T., Islam, K. R., Altyeb, A. B. A., & Chowdhury, M. E. H. (2024) · Springer eBooks · doi:10.1007/978-3-031-59967-5_8 · Cited by 5
Self-citationSurvey on Computational Applications of Tensor-Network Simulations
García, M. D., & Romero, A. M. (2024) · IEEE Access · doi:10.1109/access.2024.3519676 · Cited by 13
Yadav, S., Rizvi, S. A. M., & Agarwal, P. K. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10253-4 · Cited by 5
Gurucharan, K., & Sreenivas, A. (2025) · International Journal of Information Technology · doi:10.1007/s41870-025-02763-y · Cited by 5
Verenich, E., Velasquez, A., Khan, N., & Hussain, F. (2020) · International Conference on Machine Learning and Applications · doi:10.1109/icmla51294.2020.00218 · Cited by 5
COVID-19 Diagnosis Using Transfer-Learning Techniques
Faisal, M., Albogamy, F. R., ElGibreen, H., Algabri, M., Alvi, S. A. M., & Alsulaiman, M. (2021) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2021.017898 · Cited by 5
Yasser, I., El-Khalek, A. A. A., Khalil, A. T., Abo‐Elsoud, M. A., Salama, A. A., & Khalifa, F. (2021) · Studies in systems, decision and control · doi:10.1007/978-3-030-77302-1_7 · Cited by 5
Özcan, A. N. Ş., & Aslan, K. (2021) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405617666210806123720 · Cited by 5
COVID-19 detection method based on SVRNet and SVDNet in lung x-rays
Rao, K., Hu, Z., Wen, C. B., Xie, K., Guo, X., & He, J. (2021) · Journal of medical imaging · doi:10.1117/1.jmi.8.s1.017504 · Cited by 5
Classification of Chest X-Ray Images to Detect Pneumonia using CNN and Transfer Learning
Rehman, M., Pan, Q., Chen, D., & Manzoor, A. (2021) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm52615.2021.9669680 · Cited by 5
X-Ray Görüntülerinden COVID-19 Tespiti için Derin Öğrenme Temelli Bir Yaklaşım
Demir, F. B., & Yılmaz, E. (2022) · European Journal of Science and Technology · doi:10.31590/ejosat.1039522 · Cited by 7
Diagnosing Covid-19 using AI based Medical Image Analysis
Srivastava, V., & Ruchilekha (2022) · Joint International Conference on Data Science & Management of Data (CODS-COMAD) · doi:10.1145/3493700.3493730 · Cited by 5
CThe impact of using Convolutional Neural Networks in COVID-19 tasks: A Survey
Al-Khamees, H. A. A., Al-Jwaid, W., & Al-Shamery, E. S. (2022) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/110194 · Cited by 5
Ali, H. A., Hariri, W., Zghal, N. S., & Aissa, D. B. (2022) · International Conference on Sciences of Electronics, Technologies of Information and Telecommunications · doi:10.1109/setit54465.2022.9875477 · Cited by 5
Fukuchi, T., Ikechukwu, O. M., Wang, J., Dang, K. N., & Abdallah, A. B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16014-1_25 · Cited by 5
Preliminary Stages for COVID-19 Detection Using Image Processing
Alhaj, T. A., Idris, I., Elhaj, F. A., Elhassan, T. A. M., Remli, M. A., Siraj, M. M., & Rahim, M. S. M. (2022) · Diagnostics · doi:10.3390/diagnostics12123171 · Cited by 5
Das, G., Swain, M., Panda, R., Naik, M. K., & Agrawal, S. (2023) · Soft Computing · doi:10.1007/s00500-023-08135-7 · Cited by 5
Shyni, H. M., & Chitra, E. (2023) · OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry (OTCON) · doi:10.1109/otcon56053.2023.10113915 · Cited by 5
FedISM: Enhancing Data Imbalance via Shared Model in Federated Learning
Chung, W., Lin, Y., & Fang, S.-H. (2023) · Mathematics · doi:10.3390/math11102385 · Cited by 5
How much data do I need? A case study on medical data
Cengiz, A. B., & McGough, A. S. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386440 · Cited by 5
Detection of Covid-19 based on convolutional neural networks using pre-processed chest X-ray images
Raj, A., Sugumar, R., Padmkala, S., Giri, J. P., Ahmad, N., & Badawy, A. S. (2024) · AIP Advances · doi:10.1063/5.0200397 · Cited by 9
Brain Tumour Classification Using Convolutional Neural Network
Pandiyarajan, M., Reddy, P. A., Valarmathi, R. S., Manideep, N., & Mokshitha, P. (2024) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt61001.2024.10725486 · Cited by 5
Maity, A., Nair, T. R., & Chandra, A. (2020) · International Journal of Scientific Research in Science and Technology · doi:10.32628/ijsrst207614 · Cited by 5
Dan-Sebastian, B., Mitrea, D. A., Nedevschi, S., & Radu, B. (2020) · IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) · doi:10.1109/iccp51029.2020.9266242 · Cited by 5
Medical Image Processing and Deep Learning to Diagnose COVID-19 with CT Images
Sebdani, A. M., & Mostafavi, A. (2021) · doi:10.1109/ipria53572.2021.9483563 · Cited by 5
A Survey on Deep Learning Advances and Emerging Issues in Pneumonia and COVID19 Prediction
Makhanov, N., Tu, N. A., & Wong, K. (2022) · IEEE International Conference on Big Data and Smart Computing (BigComp) · doi:10.1109/bigcomp54360.2022.00029 · Cited by 5
Avianty, D. N., Wijaya, I. G. P. S., Bimantoro, F., Lestari, R. P., & Cahyawati, T. D. (2021) · Advances in health sciences research/Advances in Health Sciences Research · doi:10.2991/ahsr.k.220206.030 · Cited by 5
Detection of COVID-19 and Prediction of Pneumonia from Chest X-Rays using Deep Learning
Deo, G. S., Totlani, J. A., & Mahamuni, C. V. (2022) · IEEE International Conference on Communication Systems and Network Technologies (CSNT) · doi:10.1109/csnt54456.2022.9787593 · Cited by 5
Cavallo, A. U., Troisi, J., Forcina, M., Mari, P., Forte, V., Sperandio, M., Pagano, S., Cavallo, P., Floris, R., & Garaci, F. G. (2020) · doi:10.21203/rs.3.rs-37657/v1 · Cited by 5
Makkar, A., & Santosh, K. (2022) · Research Square · doi:10.21203/rs.3.rs-1943509/v1 · Cited by 5
POLCOVID: a multicenter multiclass chest X-ray database (Poland, 2020–2021)
Suwalska, A., Tobiasz, J., Prazuch, W., Socha, M., Foszner, P., Piotrowski, D., Gruszczyńska, K., Sliwinska, M., Walecki, J. M., Popiela, T. J., et al. (2023) · Scientific Data · doi:10.1038/s41597-023-02229-5 · Cited by 5
Rami Naidji, M., & Elberrichi, Z. (2024) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/1501102 · Cited by 4
Alam, H. M. T., Srivastav, D., Mohamed Selim, A., Kadir, M. A., Shuvo, M. M. H., & Sonntag, D. (2025) · Companion Proceedings of the 17th ACM SIGCHI Symposium on Engineering Interactive Computing Systems · doi:10.1145/3731406.3731970 · Cited by 4
Self-citationGFD-SSL: generative federated knowledge distillation-based semi-supervised learning
Karami, A., Ramezani, R., & Dastjerdi, A. B. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02256-7 · Cited by 4
LECLIP: Boosting Zero-Shot Anomaly Detection With Local Enhanced CLIP
Liu, Y., Li, Q., Wang, Z., Kato, J., Zhang, J., & Wang, W. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3571124 · Cited by 4
GANCHEST: a multi-GAN-based framework for chest CXR image generation and validation
Al-Shargabi, A. A., Alshobaili, J. F., Alabdulatif, A., Alrobah, N. A., & Ibrahim, D. M. (2025) · Journal of Electrical Systems and Information Technology · doi:10.1186/s43067-025-00227-8 · Cited by 4
Akhtom, D., Singh, M. M., & Chew, X. (2025) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-025-02807-6 · Cited by 4
Chen, Y., Khan, W. A., Ali, F., Afsar, H., Ejaz, M., Alshamrani, A., & Kamal, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-28723-x · Cited by 4
Contrastive learning strategies for better image classification with imbalanced datasets
Roy, S. S., Jadhav, R., & Meena, T. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2025.114450 · Cited by 4
Transformer optimization algorithm for selecting tokens based on genetic algorithm
Zhou, T., Niu, Y., Lu, H., Guo, Y., Liu, L., & Zhou, H. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.114632 · Cited by 4
Metadata
- Authors
- Chowdhury, M. E. H., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M. A., Mahbub, Z. B., Islam, K. R., Khan, M. S., Iqbal, A., Emadi, N. A., Reaz, M. B. I., & Islam, M. T.
- Affiliations
- Qatar University (QA)
- University of Dhaka (BD)
- Hamad General Hospital (QA)
- North South University (BD)
- University of Engineering and Technology Peshawar (PK)
- National University of Malaysia (MY)
- ORCID
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 132665-132676
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3013277995
- Cited by
- 1,913
- References
- 72
- Citations by year
- 2026: 122 · 2025: 188 · 2024: 276 · 2023: 364 · 2022: 432 · 2021: 424 · 2020: 107
- FWCI
- 161.58
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Qatar University QSpace (Qatar University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Qatar National Research Fund, a member of Qatar Foundation, Doha, Qatar (NPRP12S-0227-190164)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 detection and testing
- Keywords
- COVID-19 pneumonia, viral pneumonia, chest radiography, computer-aided diagnosis, COVID-19 screening, transfer learning, convolutional neural networks, image augmentation, medical image classification, COVID-19 diagnosis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
