ARCHIVES
Original Article
Multi-Model Framework for Chest X-ray Interpretation with Clinically Calibrated Tuberculosis Detection
Om Kumar1
Dr. Vimal Pumposh2
Nrip Nihalani3
Aditya Patkar4
1 2 3 4 Plus91 Technologies Pvt Ltd, Pune, India.
Published Online: July-August 2026
Pages: 56-70
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260704009
References
1. P. Rajpurkar et al., “CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning,” arXiv:1711.05225,
2017.
2. J. Irvin et al., “CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison,” in Proc. AAAI, 2019.
3. G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” in Proc. CVPR, 2017.
4. A. Dosovitskiy et al., “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,” in Proc. ICLR, 2021.
5. R. R. Selvaraju et al., “Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization,” in Proc. ICCV, 2017.
6. X. Wang et al., “ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and
Localization of Common Thorax Diseases,” in Proc. CVPR, 2017.
7. A. E. W. Johnson et al., “MIMIC-CXR: A Large Publicly Available Database of Labeled Chest Radiographs,” arXiv preprint
arXiv:1901.07042, 2019.
8. S. Jaeger et al., “Automatic Tuberculosis Screening Using Chest Radiographs,” IEEE Trans. Med. Imaging, vol. 33, no. 2, pp. 233–
245, 2014.
9. S. Candemir et al., “Lung Segmentation in Chest Radiographs Using Anatomical Atlases with Nonrigid Registration,” IEEE Trans.
Med. Imaging, vol. 33, no. 2, pp. 577–590, 2014.
10. Z. Liu et al., “TBX11K: A Large-scale Dataset for Tuberculosis Detection in Chest X-rays,” arXiv preprint arXiv:2104.02905, 2021.
11. Y. Smit, S. Jain, P. Rajpurkar, A. Pareek, A. Y. Ng, and M. P. Lungren, “CheXbert: Combining Automatic Labelers and Expert
Annotations for Accurate Radiology Report Labeling Using BERT,” in Proc. EMNLP, 2020.
12. C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger, “On Calibration of Modern Neural Networks,” in Proc. ICML, 2017.
13. J. Huang et al., “TransCheX: Detecting COVID-19 via Transformer-based Deep Learning,” arXiv preprint arXiv: 2008.10537, 2020.
14. G. Wilson and D. J. Cook, “A Survey of Unsupervised Deep Domain Adaptation,” ACM Transactions on Intelligent Systems and
Technology, vol. 11, no. 5, pp. 1–46, 2020.
15. World Health Organization, “WHO Consolidated Guidelines on Tuberculosis: Module 2: Screening,” WHO, Geneva, Switzerland,
2021.
16. S. Fort, H. Hu, and B. Lakshminarayanan, “Deep Ensembles: A Loss Landscape Perspective,” arXiv preprint arXiv: 1912.02757, 2019.
17. B. J. Erickson et al., “Trustworthy Artificial Intelligence in Medical Imaging,” Nature Reviews Clinical Oncology, vol. 18, pp. 95–
115, 2021.
18. A. Tiu et al., “Expert-level Detection of Pathologies from Unannotated Chest X-ray Images via Self-supervised Learning,” Nature
Biomedical Engineering, 2022.
19. H. Stacke et al., “Measuring Domain Shift for Deep Learning in Histopathology,” IEEE Journal of Biomedical and Health Informatics,
2021.
20. G. Liang et al., “A Positive-Unlabeled Learning Approach for Tuberculosis Detection from Chest Radiographs,” Computers in Biology
and Medicine, vol. 124, 2020.
21. C. Qin et al., “Deep Learning for Classification of Tuberculosis Chest X-ray Images,” in Proc. IEEE EMBC, 2019.
22. F. Pasa, V. Golkov, F. Pfeiffer, D. Cremers, and D. Pfeiffer, “Efficient Deep Network Architectures for Fast Chest X-ray Tuberculosis
Screening and Visualization,” Scientific Reports, vol. 9, no. 1, 2019.
2017.
2. J. Irvin et al., “CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison,” in Proc. AAAI, 2019.
3. G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” in Proc. CVPR, 2017.
4. A. Dosovitskiy et al., “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,” in Proc. ICLR, 2021.
5. R. R. Selvaraju et al., “Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization,” in Proc. ICCV, 2017.
6. X. Wang et al., “ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and
Localization of Common Thorax Diseases,” in Proc. CVPR, 2017.
7. A. E. W. Johnson et al., “MIMIC-CXR: A Large Publicly Available Database of Labeled Chest Radiographs,” arXiv preprint
arXiv:1901.07042, 2019.
8. S. Jaeger et al., “Automatic Tuberculosis Screening Using Chest Radiographs,” IEEE Trans. Med. Imaging, vol. 33, no. 2, pp. 233–
245, 2014.
9. S. Candemir et al., “Lung Segmentation in Chest Radiographs Using Anatomical Atlases with Nonrigid Registration,” IEEE Trans.
Med. Imaging, vol. 33, no. 2, pp. 577–590, 2014.
10. Z. Liu et al., “TBX11K: A Large-scale Dataset for Tuberculosis Detection in Chest X-rays,” arXiv preprint arXiv:2104.02905, 2021.
11. Y. Smit, S. Jain, P. Rajpurkar, A. Pareek, A. Y. Ng, and M. P. Lungren, “CheXbert: Combining Automatic Labelers and Expert
Annotations for Accurate Radiology Report Labeling Using BERT,” in Proc. EMNLP, 2020.
12. C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger, “On Calibration of Modern Neural Networks,” in Proc. ICML, 2017.
13. J. Huang et al., “TransCheX: Detecting COVID-19 via Transformer-based Deep Learning,” arXiv preprint arXiv: 2008.10537, 2020.
14. G. Wilson and D. J. Cook, “A Survey of Unsupervised Deep Domain Adaptation,” ACM Transactions on Intelligent Systems and
Technology, vol. 11, no. 5, pp. 1–46, 2020.
15. World Health Organization, “WHO Consolidated Guidelines on Tuberculosis: Module 2: Screening,” WHO, Geneva, Switzerland,
2021.
16. S. Fort, H. Hu, and B. Lakshminarayanan, “Deep Ensembles: A Loss Landscape Perspective,” arXiv preprint arXiv: 1912.02757, 2019.
17. B. J. Erickson et al., “Trustworthy Artificial Intelligence in Medical Imaging,” Nature Reviews Clinical Oncology, vol. 18, pp. 95–
115, 2021.
18. A. Tiu et al., “Expert-level Detection of Pathologies from Unannotated Chest X-ray Images via Self-supervised Learning,” Nature
Biomedical Engineering, 2022.
19. H. Stacke et al., “Measuring Domain Shift for Deep Learning in Histopathology,” IEEE Journal of Biomedical and Health Informatics,
2021.
20. G. Liang et al., “A Positive-Unlabeled Learning Approach for Tuberculosis Detection from Chest Radiographs,” Computers in Biology
and Medicine, vol. 124, 2020.
21. C. Qin et al., “Deep Learning for Classification of Tuberculosis Chest X-ray Images,” in Proc. IEEE EMBC, 2019.
22. F. Pasa, V. Golkov, F. Pfeiffer, D. Cremers, and D. Pfeiffer, “Efficient Deep Network Architectures for Fast Chest X-ray Tuberculosis
Screening and Visualization,” Scientific Reports, vol. 9, no. 1, 2019.
Related Articles
2026
AI-Based Stomach Cancer Detection Using Biomarkers, Medical Images, and Voice Analysis
2026
Hydrogen-Efficient Eco-Driving and Route Planning for Fuel-Cell Electric Vehicles Using Multi-Objective Optimization Under Traffic and Terrain Uncertainty
2026
A Data-Driven Machine Learning Framework for Assessing Patent Commercial Value and Technological Significance
2026
Evaluating Student Academic Performance Through a Benchmark of Fuzzy Reasoning Models
2026
A Hybrid Soft Computing Approach for Managing Uncertainty in Data Analytics
2026
Soft Computing Approaches for Robust Analysis of Imbalanced and Noisy Data
Share Article
Or copy link
https://www.theijire.com/archives/multi-model-framework-for-chest-x-ray-interpretation-with-clinically-calibrated-tuberculosis-detection
*Instagram doesn't support direct link sharing from web. Copy the link and share it in your Instagram story or post.