The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Pneumonia Detection Using CNN based Feature Extraction
Open accessAbstract
Pneumonia is a life-threatening infectious disease affecting one or both lungs in humans commonly caused by bacteria called Streptococcus pneumoniae. One in three deaths in India is caused due to pneumonia as reported by World Health Organization (WHO). Chest X-Rays which are used to diagnose pneumonia need expert radiotherapists for evaluation. Thus, developing an automatic system for detecting pneumonia would be beneficial for treating the disease without any delay particularly in remote areas. Due to the success of deep learning algorithms in analyzing medical images, Convolutional Neural Networks (CNNs) have gained much attention for disease classification. In addition, features learned by pre-trained CNN models on large-scale datasets are much useful in image classification tasks. In this work, we appraise the functionality of pre-trained CNN models utilized as feature-extractors followed by different classifiers for the classification of abnormal and normal chest X-Rays. We analytically determine the optimal CNN model for the purpose. Statistical results obtained demonstrates that pretrained CNN models employed along with supervised classifier algorithms can be very beneficial in analyzing chest X-ray images, specifically to detect Pneumonia.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
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
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.369 · Cited by 4,616
CNN Features Off-the-Shelf: An Astounding Baseline for Recognition
Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2014.131 · Cited by 4,451
Lakhani, P., & Sundaram, B. (2017) · Radiology · doi:10.1148/radiol.2017162326 · Cited by 1,775
Classification using deep learning neural networks for brain tumors
Mohsen, H., El-Dahshan, E.-S. A., El-Horbaty, E.-S. M., & Salem, A.-B. M. (2018) · Future Computing and Informatics Journal · doi:10.1016/j.fcij.2017.12.001 · Cited by 1,074
Image analysis and machine learning for detecting malaria
Poostchi, M., Silamut, K., Maude, R. J., Jaeger, S., & Thoma, G. (2018) · Translational Research · doi:10.1016/j.trsl.2017.12.004 · Cited by 559
Yu-Jen Chen, Y.-J., Hua, K.-L., Hsu, C.-H., Cheng, W.-H., & Hidayati, S. C. (2015) · OncoTargets and Therapy · doi:10.2147/ott.s80733 · Cited by 542
Cited by
Ucar, F., & Korkmaz, D. (2020) · Medical Hypotheses · doi:10.1016/j.mehy.2020.109761 · Cited by 767
Loey, M., Smarandache, F., & M. Khalifa, N. E. (2020) · Symmetry · doi:10.3390/sym12040651 · Cited by 538
“[46] proposed pre-trained ConvNet models (VGG16, Xception, Res50, Dense-121, and Dense-169) as feature-extractors followed by different classifiers (SVM, Random Forest, k-nearest neighbors, Naïve Bayes) for the detection of normal and abnormal pneumonia X-Rays images.”
MethodA scoping review of transfer learning research on medical image analysis using ImageNet
Morid, M. A., Borjali, A., & Del Fiol, G. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104115 · Cited by 508
Pneumonia detection in chest X-ray images using convolutional neural networks and transfer learning
Jain, R., Nagrath, P., Kataria, G., Sirish Kaushik, V., & Jude Hemanth, D. (2020) · Measurement · doi:10.1016/j.measurement.2020.108046 · Cited by 325
Deep Ensemble Model for Classification of Novel Coronavirus in Chest X‐Ray Images
Ahmad, F., Farooq, A., & Ghani, M. U. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/8890226 · Cited by 65
Metadata
- Authors
- Varshni, D., Thakral, K., Agarwal, L., Nijhawan, R., & Mittal, A.
- Venue
- 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT)
- Publisher
- IEEE
- Published
- 2019-02
- Pages
- 1-7
- OpenAlex
- W2980584449
- Cited by
- 437
- References
- 19
- FWCI
- 73.39
- Type
- conference paper
- Language
- en
- Open access
- bronze
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.
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Pneumonia Detection Using CNN based Feature Extraction. https://app.cochat.ai/papers/10.1109/icecct.2019.8869364
- The paper
- Varshni, D., Thakral, K., Agarwal, L., Nijhawan, R., & Mittal, A. (2019). Pneumonia Detection Using CNN based Feature Extraction. 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). https://doi.org/10.1109/icecct.2019.8869364
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
