Cited by 1,069COVID-19 diagnosis using AIAdvanced Neural Network ApplicationsRadiomics and Machine Learning in Medical Imaging

The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.

Deep convolutional neural network based medical image classification for disease diagnosis

Open access

Yadav, Samir S., Jadhav, Shivajirao M.·2019·Journal of Big Data

DOI 10.1186/s40537-019-0276-2

Open this paper in CoChat — free account Open PDF

Abstract

Abstract Medical image classification plays an essential role in clinical treatment and teaching tasks. However, the traditional method has reached its ceiling on performance. Moreover, by using them, much time and effort need to be spent on extracting and selecting classification features. The deep neural network is an emerging machine learning method that has proven its potential for different classification tasks. Notably, the convolutional neural network dominates with the best results on varying image classification tasks. However, medical image datasets are hard to collect because it needs a lot of professional expertise to label them. Therefore, this paper researches how to apply the convolutional neural network (CNN) based algorithm on a chest X-ray dataset to classify pneumonia. Three techniques are evaluated through experiments. These are linear support vector machine classifier with local rotation and orientation free features, transfer learning on two convolutional neural network models: Visual Geometry Group i.e., VGG16 and InceptionV3, and a capsule network training from scratch. Data augmentation is a data preprocessing method applied to all three methods. The results of the experiments show that data augmentation generally is an effective way for all three algorithms to improve performance. Also, Transfer learning is a more useful classification method on a small dataset compared to a support vector machine with oriented fast and rotated binary (ORB) robust independent elementary features and capsule network. In transfer learning, retraining specific features on a new target dataset is essential to improve performance. And, the second important factor is a proper network complexity that matches the scale of the dataset.

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

Crossref · 10.1186/s40537-019-0276-2

Cites

30 of 30 references verified0 citation errors · 0 not checkable

Statistics

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.

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Lecture Notes in Computer Science 1205
(1999) · Industrial Robot the international journal of robotics research and application · doi:10.1108/ir.1999.04926fae.001 · Cited by 39,228
ORB: An efficient alternative to SIFT or SURF
Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126544 · Cited by 11,037
Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning
Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759
Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.010 · Cited by 4,842
ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
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
Dynamic Routing Between Capsules
Sabour, S., Frosst, N., & Hinton, G. E. (2017) · arXiv · doi:10.48550/arxiv.1710.09829 · Cited by 4,167
Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review
Rawat, W., & Wang, Z. (2017) · Neural Computation · doi:10.1162/neco_a_00990 · Cited by 3,503
GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification
Frid-Adar, M., Diamant, I., Klang, E., Amitai, M. M., Goldberger, J., & Greenspan, H. K. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.09.013 · Cited by 2,055

Show all 30 references

Research works printed but not linked: 4

World Health Organization. Standardization of interpretation of chest radiographs for the diagnosis of pneumonia in children. Geneva: World Health Organization; 2001.
Center for Disease Control Prevention. Pneumonia can be prevented-vaccines can help. New York: Center for Disease Control Prevention; 2012.
Perez L, Wang J. The effectiveness of data augmentation in image classification using deep learning. 2017. arXiv preprint arXiv:1712.04621. · arXiv
Beutel J, Kundel H L, Van Metter R L, Fitzpatrick J M. Handbook of Medical Imaging: medical image processing and analysis, vol. 2. Bellingham: Spie Press; 2000.

Sorted by citations

Items 901–999 of 999

Classification of Thoracic Pathologies Including COVID-19 in Chest X-ray Images by Using Convolutional Neural Networks
Vajar, P. (2023) · doi:10.32920/24050730.v1
Automatic contour correction of pectus excavatum using computer-aided diagnosis and convolutional neural network
Cai, S., Liao, Y., Lai, L., Zhou, H., & Xie, L. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107032
A Power-efficient Image Classifier using Neural Network with Pipelined FFT ArchitecturePreprint
Hai, M. S., & Reddy, T. R. (2023) · doi:10.36227/techrxiv.24165294.v1
A Power-efficient Image Classifier using Neural Network with Pipelined FFT ArchitecturePreprint
Hai, M. S., & Reddy, T. (2023) · doi:10.36227/techrxiv.24165294
Toward Multi-Level Automatic Updates of Deep Learning Models Using New Micro, Meso, and Macro Stability Metrics
Jabeur, N. H., & Abdelli, A. (2023) · International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) · doi:10.1109/iceccme57830.2023.10253217
Classification of human protein cell images using deep neural networks
Dong, Y., Che, X., Fu, Y., Liu, H., & Sun, L. (2023) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-223464
Transfer Learning Using Convolutional Neural Network to Classify Leaf Diseases on Ridge Gourd Plant
Katare, R. U., Mahajan, A. A., & Adamuthe, A. C. (2023) · Springer eBooks · doi:10.1007/978-3-031-34644-6_27
Pneumonia Detection Using Deep Learning: A Bibliometric Study
Jatwani, R., & Sharma, P. K. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-43145-6_16
Improvement of the Process of Diagnosing Patient’s Condition via Computer Tomography Lung Scans Using Neural Networks
Nahajowski, M., Kędziora, M., & Jóźwiak, I. J. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-42430-4_40
MultiVT: Multiple-Task Framework for Dentistry
Rella, E. M., Chhatkuli, A., Konukoğlu, E., & Gool, L. V. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-45857-6_2
Brain Tumors Classification Using Convolutional Neural Networks
Vigneshwari, K., & Pavithra, R. (2023) · Middle East Journal of Applied Science & Technology · doi:10.46431/mejast.2023.6310
Classification of Diabetic Retinopathy Based on Lesion-Sliced Detection
Dong, S., Song, Z., Liu, Y., Liu, L., Zhang, C., Zhang, B., & Zhang, C. (2023) · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) · doi:10.1109/icnc-fskd59587.2023.10281175
Deep Neural Network for Medical Imaging: A Systematic Review
(2023) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets45588
Self-Supervised Learning for Improving Automated Predictions of Cobb Angles from X-Ray Images: Machine Learning Study (Preprint)Preprint
Kalle, L., & Washington, P. Y. (2023) · doi:10.2196/preprints.53791
Invasive Ductal Carcinoma Classification from Whole Slide Image Based on BRISQUE and Convolutional Neural Networks
Thuy, L. N. L., Sang, V. N. T., Bao, P. T., & Trinh, T. D. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-8296-7_43
A Deep-Learning Error Detection System in Radiation Therapy
Kump, P., Xia, J., Yaddanapudi, S., & Bai, E. (2023) · Annals of Biomedical Research · doi:10.61545/abr-5-126
MedPipe: End-to-End Joint Search of Data Augmentation and Neural Architecture for 3D Medical Image ClassificationPreprint
Chu, X., & He, X. (2023) · doi:10.36227/techrxiv.19513780.v2
Detection of the Biliary Atresia Using Deep Convolutional Neural Networks Based on Statistical Learning Weights via Optimal Similarity and Resampling Methods
Hayashi, K., Hoshino, E., Suzuki, M., Nakanishi, E., Sakai, K., & Obatake, M. (2023) · Studies in classification, data analysis, and knowledge organization · doi:10.1007/978-3-031-09034-9_20
Classification of Coronary Stenosis using a Support Vector Machine with Automatic Parameter Tuning
Gil-Rios, M.-A., Cruz-Aceves, I., Chalopin, C., Brieva, J., Gomez-Coronel, S. L., & Moya-Albor, E. (2023) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1109/sipaim56729.2023.10373550
A parameter adaptive tuning algorithm for medical image recognition model testing
Huang, T., Ding, Y., Dai, C., Zhuang, Z., & Hu, K. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385867
DCNET-DCGAN: A Novel Deep Convolutional Neural Network for COVID-19 Classification
Sharmila, V. J. (2023) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i9.9922
The Emerging Applications of Machine Learning in the Diagnosis of Multiple Sclerosis
Sharma, A., Sharma, N., Arora, A., & Pal, R. (2024) · Studies in computational intelligence · doi:10.1007/978-981-99-9029-0_6
Arterial Contrast Optimization in Time-Resolved Contrast-Enhanced Cerebral MR Angiography Using Generative Neural NetworksPreprint
Park, C. J., Choi, K. S., Park, J., Choi, S. H., Hwang, I. P., & Shin, T. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4752261
Detecting the symptoms of COVID-19 during pandemic environment using smart spectacle thermal images and deep capsule networks
Dwarakanath, B., Pandimurugan, V., Mohandas, R., Sambath, M., Baiju, B. V., & Chinnasamy, A. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18812-w
Comparative analysis of different model depths on convolutional neural network for handwritten digit recognition
Deng, W. (2024) · Applied and Computational Engineering · doi:10.54254/2755-2721/51/20241178
Classification of Datasets Based on Combination Algorithm of Clustering and Neural Network
Yang, Y., & Li, L. (2023) · Information (Koganei) · doi:10.47880/inf2604-04
Farming realities: An in-depth exploration of constraints encountered by both member and non-member farmers of FPO constraints encountered by farmers of FPO
Kolekar, P. L., Chavan, R. V., & Shivram, P. D. (2024) · International Journal of Agriculture Extension and Social Development · doi:10.33545/26180723.2024.v7.i3g.482
Evaluation of deep learning frameworks coupled with an interactive user interface to predict clinical complications after aneurysmal subarachnoid hemorrhage
Faiz, R., Danala, G., Ray, B. S., Islam, W., & Ebert, D. S. (2024) · SPIE Medical Imaging: Clinical and Biomedical Imaging · doi:10.1117/12.3006983
Medical image analysis steps: Medical image acquisition to classification (or regression) in neuro-oncology
Das, S., Meghna, & Saxena, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-18508-3.00002-4
GCNN-based Combined Denoising and Classification for Improved MRI Brain Tumor Identification
Devi, G. D., Doss, S. S., Sanjitha, S., & Chaithanya, N. S. (2023) · International Conference on Innovative Mechanisms for Industry Applications · doi:10.1109/icimia60377.2023.10425821
Application of space invariant artificial neural networks for network image interaction design
Ding, Z. (2024) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-239538
Clamp Dot Image Classification Using Neural Network
Srikam, K., & Saenthon, A. (2024) · Sensors and Materials · doi:10.18494/sam5000
Detection and Classification of Chest Diseases using Machine Learning Algorithm
Aggarwal, R., Gupta, R., Jain, C., & Kumar, A. (2023) · International Conference on Information Management & Machine Intelligence (ICIMMI) · doi:10.1145/3647444.3647947
_______________________________________________________________________ HEALTH CHECK-UP APPOINTMENT TRACKING SYSTEM: A COMPREHENSIVE WEB APPLICATION FOR EFFICIENT PATIENT-DOCTOR INTERACTION
Roopini, B. K., Ruchitha, Shobharani, Kumari, S., & S, M. A. M. (2024) · International Journal Of Trendy Research In Engineering And Technology · doi:10.54473/ijtret.2024.8206
Examining the Various Neural Network Algorithms Considering the Superiority of Mouth Brooding Fish in Data Classification
Liu, L., & Zhu, Y. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.0150522
An Adaptive Framework for Medical Image Clustering-Based Brain Tumor Classification Using Transfer LearningPreprint
Abd-Alamir, S. A., Al-Shammari, A. M., & Washi, F. M. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4824048
Lane Detection and Tracking Algorithms for Driver Assistance System
Rukasar, A., & Gautam, P. R. (2023) · International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · doi:10.1109/icac3n60023.2023.10541447
Image Recognition and Product Recommendation Algorithms Based on Deep Learning
Diwu, W., Zhang, R., Feng, L., & Mu, Q. (2024) · International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) · doi:10.1109/icdcece60827.2024.10549427
A Multi-level Deep Neural Network to Diagnose Coronavirus Disease with Imbalanced DataPreprint
Zabbah, I., Layeghi, K., & Ebrahimpour, R. (2024) · Research Square · doi:10.21203/rs.3.rs-4542329/v1
Peak response regularization for localization
Yu, J., Yao, J., Zhao, C., Zhao, X., & Hu, Q. (2024) · Scientific Reports · doi:10.1038/s41598-024-65770-2
Effect of Biocapsule and Nanozeolite in Improving Growth, Development and Yield of Low Chilling Apple (Malus x domestica Borkh.) cv. HRMN-99 in Prayagraj Agro-Climatic Condition
Gupta, M., & Mishra, S. (2024) · Journal of Advances in Biology & Biotechnology · doi:10.9734/jabb/2024/v27i81213
Development of the method of blood clot origin classification in ischemic stroke
Kozhamseitova, A. (2024) · IEEE International Conference on Smart Information Systems and Technologies (SIST) · doi:10.1109/sist61555.2024.10629352
Performance Analysis of a Single-Input Thermal Image Classifier with Patient Information for the Detection of Breast Cancer
Cherian, A. S., Mammoottil, M. J., Kulangara, L. J., Mohandas, P., Anni, J. S., Raj, V., & Murugathas, T. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-68639-9_10
Feature Autonomous Screening and Sequence Integration Network for Medical Image Classification
You, H., Chen, X., Yu, K., Fu, G., Mao, F., Ning, X., Bai, X. P., & Cai, W. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3448490
Advancing Multibody Binary Classification in Imbalanced X-Rays Dataset Using MobileNetV2
Karen, A., Aherman, V. N., Qomariyah, N. N., Yessenbayev, Z., & Chaetajaka, P. A. (2024) · International Conference on Smart Computing and Communications (ICSCC) · doi:10.1109/icscc62041.2024.10690321
Research on Wheat Ear Recognition Method Based on Image Color Features and Neural Networks
Liu, S., & Chen, Y. (2024) · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) · doi:10.1109/icnc-fskd64080.2024.10702229
Learning from an Expert Agent: An Imitated-Reinforcement Learning Application
Choutri, K., Siddique, T., Fareh, R., Dylov, D. V., & Bettayeb, M. (2024) · Advances in Science and Engineering Technology International Conferences (ASET) · doi:10.1109/aset60340.2024.10708696
Applications of Big Data Analytics and Artificial Intelligence in Medical Domain: A Survey
Azeroual, A., Nsiri, B., Thami, R. O. H., & Benaji, B. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-5035-1_39
Disease detection and treatment methods
Makubhai, S. S., Pathak, G. R., & Chandre, P. R. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-22038-8.00005-2
Early Breast Cancer Detection Among Patients Using a Deep Learning Image Processing Model
Bhati, P. S., Shrivastava, V., & Pandey, A. (2024) · Smart innovation, systems and technologies · doi:10.1007/978-981-97-6810-3_17
FaceEvoke: Eliciting Emotions Through Facial Analysis
Gupta, A., Srivastava, A., & Shukla, M. K. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-75861-4_31
Diabetes prediction based on convolutional neural network and jetson nano kit
Nafea, D. Z., & Khidhir, A. M. (2024) · AIP conference proceedings · doi:10.1063/5.0238018
Application of binary subnetworks to classify images potentially containing melanocytic skin lesions
Szkoła, J., Paja, T., & Paja, W. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.09.301
Few-Shot Copycat: Improving Performance of Black-Box Attack with Random Natural Images and Few Examples of Problem Domain
Leão, J. M., Correia-Silva, J. R., Souza, A. F. D., Badue, C. S., & Oliveira-Santos, T. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78183-4_26
Supervised Automated Kinetic Perimetry (SAKP) Using Simulated Visual Field Data – Presentation of a New Examination Technique
Schiefer, U., Wörner, M., & Zobor, D. (2024) · Klinische Monatsblätter für Augenheilkunde · doi:10.1055/a-2427-3556
Four different machine learning methods in cancer image recognition
Chen, S. (2024) · AIP conference proceedings · doi:10.1063/5.0226999
Classification Using U-Net CN on Multi-Resolution CT Scan Image
Surono, S., Rivaldi, M., & Irsalinda, N. (2024) · Frontiers in artificial intelligence and applications · doi:10.3233/faia241412
Convolutional Neural Networks for Image Classification Using the SPSS Method
Chandraprakash, D., Pallavi, D. R., Ramachandran, M., Sivaji, C., & Amudha, M. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-73318-5_42
Advanced Machine Learning for Brain Tumor and Alzheimer’s Disease Detection: A Comprehensive Review of Neuroimaging-based Classification Techniques
Kumar, N., Narawade, V. E., Chheda, K., Patkar, H., & Mishra, A. (2024) · Advances in Artificial Intelligence Research · doi:10.54569/aair.1582085
A New Paradigm for Deep Micro-Expression Recognition Under Occluded Conditions
Wu, C. (2024) · International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI) · doi:10.1109/ichci63580.2024.10807983
Pressure injury ulcer stage classification based on convolution neural network image recognition
Huang, Y.-J., Hung, W.-C., Hsu, I.-H., Su, C.-T., & Hsiao, F. (2025) · OPTICS & PHOTONICS International Congress · doi:10.1117/12.3036149
3D Reconstruction for Early Detection of Liver Cancer
Mohamed, R., EL-GENDY, M. S. E. M., & Taha, M. (2025) · Computer Systems Science and Engineering · doi:10.32604/csse.2024.059491
Studying Into the Diagnostic and Therapeutic Applications of Machine Learning Algorithms in Medicine
Shirke, S., Sonkamble, R. G., Patil, S. C., Prasad, J. R., Thepade, S. D., & Midhunchakkaravarthy, D. (2025) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-6308-9.ch008
Anterior view of the thyroid scintigraphy imaging with collimator-less gamma camera system using XCAT phantom and Monte-Carlo method
Jo, A., Lee, J., & Lee, W. (2025) · Nuclear Engineering and Technology · doi:10.1016/j.net.2025.103481
Mathematics in biomedical robotics and devices and its role in healthcare delivery
Sadique, M., Shah, S. R., & Islam, S. M. N. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-22206-1.00018-8
Impacts of Image Preprocessing Techniques on Lung Cancer Classification
Vijayan, N., Kuruvilla, J., George, J. K., & Sundar, S. (2024) · International Conference on Advances in Computing and Communications (ICACC) · doi:10.1109/icacc63692.2024.10845295
Analysis of Near-Fall Detection Method Utilizing Dynamic Motion Images and Transfer Learning
Kim, J.-Y., Mat, N., Kim, C., Khan, A. A., Gil, H., Lyu, J., Chung, E., Kim, K. S., Jeon, S., & Nam, Y. (2025) · IEEE Access · doi:10.1109/access.2025.3539449
Plant Leaf Disease Detection and Recommendation System using Alex Net-Honey Badger Fusion Algorithm
Mitra, D., Goyal, A., Gupta, G., Shivkant, & Poonia, R. C. (2025) · Data & Metadata · doi:10.56294/dm2025206
Methodology for Studying Neural Network Descriptors in Solving the Problem of Finding Anatomical Layers in Computed Tomography Images of the Lungs
Kosareva, A. A. (2025) · Doklady BGUIR · doi:10.35596/1729-7648-2025-23-1-60-67
Hybrid Image Features Supported Normal/Abnormal Retinal Optical Coherence Tomography Image Classification
Mārtiņš, A. (2024) · International Journal of Clinical Medical Research · doi:10.61466/ijcmr3010001
MSR: A Personalized Movie Recommendation Model Based on Gate Mechanism and Attention Network
Liu, L., Zhu, J., Mi, J., Li, J., Cao, X., & Wang, H. (2025) · International Journal of Computational Intelligence and Applications · doi:10.1142/s1469026824420045
CNN-Driven Voice Guidance: Image Based Medicine Recognition
Surapaneni, R. K., Bharathi, J., & Divya, D. (2025) · Smart innovation, systems and technologies · doi:10.1007/978-981-97-7717-4_27
Optimizing Aerospace Missions with Convolutional Neural Networks: Innovations and Advantages
Chen, P., Padun, Y., & Du, J. (2024) · International Conference on Systems and Informatics (ICSAI) · doi:10.1109/icsai65059.2024.10893852
Automatic Identification of Teff (Eragrostis Tef) Flour Adulteration Using Machine Learning
Bochera, Z. A., Gizaw, S., & Clement, O. (2024) · International Conference on Systems and Informatics (ICSAI) · doi:10.1109/icsai65059.2024.10893729
LA-TransUNet: Intracranial Hemorrhage CT Image Segmentation Network Based on Attention Mechanism
Xu, H., & Wu, J. (2024) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei64163.2024.10906179
Adaptive Feature Optimization in fMRI Brain Networks for Alzheimer's Disease Detection via GA-SA
Zhou, S., Zhou, S., Yang, Y., & Yang, Y. (2024) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei64163.2024.10906283
A cognitive technique for human monkeypox disease diagnosis and prevention in healthcare system
Kumari, S., Kaur, H., Sandhu, R., & Ghai, D. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-30146-9.00009-5
Research on Medical Image Classification for Oral Cancer
Wang, K., Shi, L., Gu, C., & Mo, F. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-2468-3_44
Classification of Brain Tumor Images Optimizing the Convolutional Neural Network EfficientNet
Wicker, A., Hanne, T., & Dornberger, R. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-2468-3_48
Reparameterization lightweight residual network for super-resolution of brain MR images
Geng, Y., Wang, P., Cong, J., Li, X., Liu, K., & Wei, B. (2025) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/adc935
Enhancing Lung Nodule Classification Through Weighted Ensemble FrameworkPreprint
Zadnorouzi, M., & Sadremomtaz, A. (2025) · Research Square · doi:10.21203/rs.3.rs-5840049/v1
Rethinking Transfer Learning for Histopathology Cancer Detection: HistoNet
Yadav, A., & Daescu, O. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-85908-3_1
Optimizing COVID-19 Detection from Chest X-Rays: A Comparative CNN-Based Analysis
Kanjani, A. R., Singh, B., Srivastava, P. C., & Bhukya, R. K. (2024) · IEEE India Conference · doi:10.1109/indicon63790.2024.10958338
Melanoma Skin Cancer Detection Application using Convolutional Neural Network (CNN)
Araneta, A. A. S. (2025) · Journal of Information Systems Engineering & Management · doi:10.52783/jisem.v10i40s.7306
Enhancing Fine-Grained Visual Classification via Curriculum Learning and Global–Local Feature Interaction
Zhang, X., Wang, S., Feng, F., & Liu, J. (2025) · The Visual Computer · doi:10.1007/s00371-025-03932-7
Topological Properties and Computation of Neural Networks Using Cover Pebbling Number Technique with an Algorithmic Approach
Kavitha, K. C., Jagatheswari, S., Dhivviyanandam, I., & Prashitha, J. R. (2025) · Circuits Systems and Signal Processing · doi:10.1007/s00034-025-03201-x
Improving Healthcare Outcomes: Doctor Classification and Performance Prediction via ML
M, V. T. R. P. K., Galib, S. C., Likhith, K., Keerthi, M., Ramya, N., & Rajesh, K. (2025) · International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) · doi:10.1109/icaiss61471.2025.11041702
Decoding MRI Data Machine Learning for Structural Biomarker Discovery in Modern Radiodiagnosis
Sharma, M., Kumar, P., & Verma, P. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5801-7.ch002
ARI-ORB: Adaptive Rotation-Invariant ORB for Enhanced Feature Matching in Dental X-Ray Imaging
Taher, S. M., & Der, C. S. (2025) · IEEE Access · doi:10.1109/access.2025.3590077
The GAN Model for Three-Dimensional Animation Graphic Design Under Convolutional Neural Network
Fan, Y., & Zhang, Y. (2025) · International Journal of Information Technologies and Systems Approach · doi:10.4018/ijitsa.386840
Intelligent denoiser of plasma waves by SS-520-3 sounding rocket experiment toward future EMC challenges in miniaturized platform chassis
Ozaki, M., Morita, R., Hirano, A., Yagitani, S., Kasahara, Y., Zushi, T., Kurita, S., & Kojima, H. (2025) · Earth Planets and Space · doi:10.1186/s40623-025-02271-0
Development and validation of explainable deep learning models for classification of atrial fibrillation subtypes using cardiac computed tomography
Takeda, K., Sobue, Y., Matsuo, H., Watanabe, E., & Kobayashi, S. (2025) · Heart Rhythm O2 · doi:10.1016/j.hroo.2025.08.031
Breast Cancer Classification Using Convolutional Neural Network: A Systematic Literature Review
Dababat, A., Eleyan, D., Eleyan, A., Amer, M., & Bejaoui, T. (2025) · International Conference on Smart Applications, Communications and Networking (SmartNets) · doi:10.1109/smartnets65254.2025.11106855
Assessment on Convolutional Neural Networks for Machine Learning-Based X-Ray Image Detection and Categorization
Shiny, X. S. A., Priyanka, S., Srilekha, M., Babu, J. P., Parashar, K., & Rao, K. V. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-5214-3_30
Mathematical modeling in healthcare engineering
Sasikala, D., Suresh Kumar, K., Raja, M. S., Vinod Kumar, D., Umamageswari, D., & Girimurugan, R. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33514-3.00004-6
Comparative Analysis of CNN, RNN, and DNN Architectures for Disease Classification
Arrafi, R. F., & Mandala, S. (2025) · International Conference on Data Science and Its Applications (ICoDSA) · doi:10.1109/icodsa67155.2025.11157505
Deep Learning Algorithms and Techniques
Syed, M., Chauhan, V., Mathur, B., Goyal, S., Narad, P., & Sengupta, A. (2025) · Medical virology · doi:10.1007/978-981-96-8919-4_3
Analyzing performance: Deep learning model’s impact on image feature selection methods
Naik, V., Deepika, D., Vishweshwar, D. U., Sujjani, P., & Vaishnavi, S. (2025) · AIP conference proceedings · doi:10.1063/5.0296672
Prediction of Lung and Colon Cancer Using Bayesian Optimization Based Deep Learning Model
Samrin, R., Kakkireni, B. K., Avanija, J., Reddy, T. N., Sanaboina, S. V. S. V. P., & Madhavi, K. R. (2025) · Smart innovation, systems and technologies · doi:10.1007/978-981-96-4410-0_53

Authors: Yadav, S. S., & Jadhav, S. M.
Affiliations: Dr. Babasaheb Ambedkar Technological University (IN)
Corresponding author: Yadav, Samir S.
ORCID: Yadav, Samir S.
Jadhav, Shivajirao M.
Venue: Journal of Big Data
Publisher: Springer Science and Business Media LLC
Published: 2019-12
Volume: 6
Issue: 1
ISSN: 2196-1115 (ISSN-L)
DOI: 10.1186/s40537-019-0276-2
OpenAlex: W2995942064
Cited by: 1,069
References: 30
Citations by year: 2026: 57 · 2025: 139 · 2024: 234 · 2023: 215 · 2022: 197 · 2021: 166 · 2020: 61
FWCI: 69.98
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor, 16 d)
Locations: Journal Of Big Data (publishedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords: medical image classification, pneumonia detection, chest radiography, convolutional neural networks, transfer learning, VGG16, InceptionV3, capsule networks, support vector machine, ORB features, data augmentation, small medical image datasets

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

Deep convolutional neural network based medical image classification for disease diagnosis

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks