A Systematic Search over Deep Convolutional Neural Network Architectures for Screening Chest Radiographs
Open accessAbstract
Chest radiographs are primarily employed for the screening of pulmonary and cardio-/thoracic conditions. Being undertaken at primary healthcare centers, they require the presence of an on-premise reporting Radiologist, which is a challenge in low and middle income countries. This has inspired the development of machine learning based automation of the screening process. While recent efforts demonstrate a performance benchmark using an ensemble of deep convolutional neural networks (CNN), our systematic search over multiple standard CNN architectures identified single candidate CNN models whose classification performances were found to be at par with ensembles. Over 63 experiments spanning 400 hours, executed on a 11.3 FP32 TensorTFLOPS compute system, we found the Xception and ResNet-18 architectures to be consistent performers in identifying co-existing disease conditions with an average AUC of 0.87 across nine pathologies. We conclude on the reliability of the models by assessing their saliency maps generated using the randomized input sampling for explanation (RISE) method and qualitatively validating them against manual annotations locally sourced from an experienced Radiologist. We also draw a critical note on the limitations of the publicly available CheXpert dataset primarily on account of disparity in class distribution in training vs. testing sets, and unavailability of sufficient samples for few classes, which hampers quantitative reporting due to sample insufficiency.
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
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?
Chakravarty et al. [23] had an AUC score of 80% with FL, while with the same dataset and ML algorithm articles [67] and [68] had 86% and 87% AUC, respectively.
“Then as well Chakravarty et al. [23] has an AUC score of 80% with FL environment, but with same dataset and ML algorithm article [67] and [68] have 86% and 87% AUC respectively.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [68]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
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 · see citing works
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 · see citing works
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138 · see citing works
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380 · see citing works
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Tan, M., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1905.11946 · Cited by 17,072 · see citing works
RISE: Randomized Input Sampling for Explanation of Black-box ModelsPreprint
Petsiuk, V., Das, A., & Saenko, K. (2018) · British Machine Vision Conference · doi:10.48550/arxiv.1806.07421 · Cited by 1,702 · see citing works
Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification
Baltruschat, I. M., Nickisch, H., Grass, M., Knopp, T., & Saalbach, A. (2019) · Scientific Reports · doi:10.1038/s41598-019-42294-8 · Cited by 455 · see citing works
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., et al. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301590 · Cited by 392 · see citing works
Radiologist shortage leaves patient care at risk, warns royal college
Rimmer, A. (2017) · BMJ · doi:10.1136/bmj.j4683 · Cited by 193 · see citing works
Automated Chest X-Ray Screening: Can Lung Region Symmetry Help Detect Pulmonary Abnormalities?
Santosh, K., & Antani, S. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2775636 · Cited by 154 · see citing works
A Novel Approach for Multi-Label Chest X-Ray Classification of Common Thorax Diseases
Allaouzi, I., & Ben Ahmed, M. (2019) · IEEE Access · doi:10.1109/access.2019.2916849 · Cited by 126 · see citing works
Rajaraman, S., Sornapudi, S., Kohli, M. D., & Antani, S. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8856715 · Cited by 23 · see citing works
Bastawrous, S., & Carney, B. W. (2017) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-016-9937-2 · Cited by 20 · see citing works
Learn To Be Uncertain: Leveraging Uncertain Labels In Chest X-rays With Bayesian Neural Networks
Yang, H., Yang, J., Pan, Y., Cao, K., Song, Q., Gao, F., & Yin, Y. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · Cited by 9 · see citing works
Flagged first, then by citations
2 of the 19 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Deep learning for chest X-ray analysis: A survey
Çallı, E., Sogancioglu, E., van Ginneken, B., van Leeuwen, K. G., & Murphy, K. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102125 · Cited by 531 · see citing works
A systematic review on federated learning in medical image analysis
Sohan, M. F., & Basalamah, A. (2023) · IEEE Access · doi:10.1109/access.2023.3260027 · Cited by 114 · see citing works
A Comprehensive Review of Explainable Artificial Intelligence (XAI) in Computer Vision
Cheng, Z. Q., Wu, Y. P., Li, Y., Cai, L., & Ihnaini, B. (2025) · Sensors · doi:10.3390/s25134166 · Cited by 111 · see citing works
Ziegler, J., Pfitzner, B., Schulz, H., Saalbach, A., & Bert Arnrich, A. (2022) · Sensors · doi:10.3390/s22145195 · Cited by 27 · see citing works
Federated Learning for Site Aware Chest Radiograph Screening
Chakravarty, A., Kar, A., Sethuraman, R., & Sheet, D. (2021) · 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9433876 · Cited by 24 · see citing works
Self-citationIBPGNET: lung adenocarcinoma recurrence prediction based on neural network interpretability
Xu, Z., Liao, H., Huang, L., Chen, Q., Lan, W., & Li, S. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae080 · Cited by 16 · see citing works
Detecting Presence of COVID-19 with ResNet-18 using PyTorch
Kochgaven, C., Mishra, P., & Shitole, S. S. (2021) · International Conference on Communication Information and Computing Technology (ICCICT) · doi:10.1109/iccict50803.2021.9510085 · Cited by 10 · see citing works
A Weakly supervised approach for thoracic diseases detection
Kabir, S., Farrokhvar, L., & Dabouei, A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118942 · Cited by 7 · see citing works
A 178-clinical-center experiment of integrating AI solutions for lung pathology diagnosis
Ibragimov, B., Arzamasov, K. M., Maksudov, B., Kiselev, S., Mongolin, A., Mustafaev, T., Ibragimova, D., Evteeva, K., Андрейченко, А. Е., & Morozov, S. P. (2023) · Scientific Reports · doi:10.1038/s41598-023-27397-7 · Cited by 7 · see citing works
- Authors
- Mitra, A., Chakravarty, A., Ghosh, N., Sarkar, T., Sethuraman, R., & Sheet, D.
- Affiliations
- Indian Institute of Technology Kharagpur (IN)
- Apollo Gleneagles Hospitals (IN)
- Intel (India) (IN)
- Venue
- 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
- Publisher
- IEEE
- Published
- 2020-07
- Volume
- 2020
- Pages
- 1225-1228
- ISSN
- 2375-7477 (ISSN-L)
- OpenAlex
- W3018274863
- Cited by
- 9
- References
- 19
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 2 · 2022: 2 · 2021: 3
- FWCI
- 1.92
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- deep convolutional neural networks, CNN architecture comparison, Xception, ResNet-18, saliency maps, RISE, explainable AI, class imbalance, resource-limited settings
- MeSH terms
- Lung, Radiography, Research, Reproducibility of Results, Neural Networks, Computer
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
