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Original Article
Early Tuberculosis Detection from Chest X-rays Using Conditional GANs and Deep Transfer Learning
Akanksha Soni1
Avinash Rai2
1 2 Electronics and Communication Engineering, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalay, Bhopal, Madhya Pradesh, India.
Published Online: September-December 2025
Pages: 171-184
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20250403028
References
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21. Priyanka Saha. “An ensemble cnn-dempster shafer based tuberculosis detection from chest x-ray images”. In: 2022 IEEE Calcutta Conference (CALCON).IEEE. 2022, pp. 228–232.
22. Kyeongjin Ann et al. “Generation of high-resolution chest X-rays using multi-scale conditional generative adversarial network with attention”. In: Journal of Broadcast Engineering 25.1 (2020), pp. 1–12.
23. Tomohiro Kikuchi et al. “Synthesis of hybrid data consisting of chest radiographs and tabular clinical records using dual generative models for COVID-19 positive cases”. In: Journal of Imaging Informatics in Medicine 37.3 (2024), pp. 1217–1227.
24. Laith Alzubaidi et al. “MedNet: pre-trained convolutional neural network model for the medical imaging tasks”. In: arXiv preprint arXiv:2110.06512(2021).
25. Lucas C Ribas, Wallace Casaca, and Ricardo T Fares. “Conditional Generative Adversarial Networks and Deep Learning Data Augmentation: A Multi- Perspective Data-Driven Survey Across Multiple Application Fields and Classification Architectures”. In: AI 6.2 (2025), p. 32.
26. Haruna Jallow et al. “GDP prediction of The Gambia using generative adversarial net- works”. In: Frontiers in Artificial Intelligence 8 (2025), p. 1546398.
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28. Gao Huang et al. “Densely connected convolutional networks”. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2017,pp. 4700–4708.
2. Maayan Frid-Adar et al. “Synthetic data augmentation using GAN for improved liver lesion classification”. In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018). IEEE. 2018, pp. 289–293.
3. Shalyne Nyambura et al. “A Likelihood-Based Multiple Change Point Algorithm for Count Data with Allowance for Over-Dispersion”. In: Open Journal of Statistics 14.5 (2024), pp. 518–545.
4. Goram Mufarah M Alshmrani et al. “A deep learning architecture for multi-class lung dis- eases classification using chest X-ray (CXR) images”. In:Alexandria Engineering Journal 64 (2023), pp. 923–935.
5. Sivaramakrishnan Rajaraman and Sameer K Antani. “Modality-specific deep learning model ensembles toward improving TB detection in chest radiographs”. In: IEEE Access 8 (2020), pp. 27318–27326.
6. Mehdi Mirza and Simon Osindero. “Conditional generative adversarial nets”. In: arXiv preprint arXiv:1411.1784 (2014).
7. Haisheng Hui et al. “Stroke-CVAE-CGAN: A Medical Imaging Data Augmentation Net- work Incorporating Stroke Lesion Distribution”. In: 2024 IEEE 11th International Con- ference on Data Science and Advanced Analytics (DSAA). IEEE. 2024, pp. 1–10.
8. Brian Ngugi et al. “Utilization of digital tools to enhance COVID-19 and tuberculosis testing and linkage to care: a cross-sectional evaluation study among Bodaboda riders in the Nairobi Metropolis, Kenya”. In: PLOS ONE 18.6 (2023), e0287305. doi: 10.1371/ journal.pone.0287305. URL: https://journals.plos.org/plosone/article?id=10. 1371/journal.pone.0287305.
9. Sagar Kora Venu. “Improving the generalization of deep learning classification models in medical imaging using transfer learning and generative adversarial networks”. In: Interna- tional Conference on Agents and Artificial Intelligence. Springer. 2021, pp. 218–235.
10. Suresh Sankaranarayanan and Akshat Khare. “Implementing Data Augmentation Techniques Using Conditional Generative Adversarial Network-Based upon Chest X-Ray Im- ages”. In: Intelligent Systems Conference. Springer. 2024, pp. 531–541.
11. Simon Karanja et al. “Utilization of digital tools to enhance COVID-19 and tuberculosis testing and linkage to care: A cross-sectional evaluation study among Bodaboda motorbike riders in the Nairobi Metropolis, Kenya”. In: Plos one 18.9 (2023), e0290575.
12. Sammy Kiprop et al. “Classification of Contraceptive Use Among Undergraduate Students Using a Supervised Machine Learning Technique”. In:American Journal of Theoretical and Applied Statistics 12.5 (2023), pp. 120–128.
13. Weigang Wen, Yihao Bai, and Weidong Cheng. “Generative adversarial learning enhanced fault diagnosis for planetary gearbox under varying working conditions”. In: Sensors 20.6 (2020), p. 1685.
14. Alex Mirugwe, Lillian Tamale, and Juwa Nyirenda. “Improving Tuberculosis Detection in Chest X-Ray Images Through Transfer Learning and Deep Learning: Comparative Study of Convolutional Neural Network Architectures”. In: JMIRx Med 6 (2025), e66029.
15. Kevser Sahinbas and Ferhat Ozgur Catak. “Transfer learning-based convolutional neural network for COVID-19 detection with X-ray images”. In: Data science for COVID-19. Elsevier, 2021, pp. 451–466.
16. Linh T Duong et al. “Detection of tuberculosis from chest X-ray images: Boosting the performance with vision transformer and transfer learning”. In:Expert Systems with Ap- plications 184 (2021), p. 115519.
17. Rachael Wanjiru Njoroge, Anthony Waititu, and Anthony Wanjoya. “Modeling Breast Cancer Risks Using Artificial Neural Network: A Case Study of Kenyatta National Hospital in Nairobi, Kenya”. In: ().
18. Osman Güler and Kemal Polat. “Classification performance of deep transfer learning methods for pneumonia detection from chest X-ray images”. In:Journal of Artificial Intelligence and Systems 4.1 (2022), pp. 107–126.
19. Amadou Kindy Barry, Anthony Waititu Gichuhi, and Lawrence Nderu. “Spatial Heterogeneity Modeling Using Machine Learning Based on a Hybrid of Random Forest and Convolutional Neural Network (CNN)”. In: Journal of Data Analysis and Information Processing 12.3 (2024), pp. 319–347.
20. Tawsifur Rahman et al. “Reliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization”. In: Ieee Access 8 (2020), pp. 191586–191601.
21. Priyanka Saha. “An ensemble cnn-dempster shafer based tuberculosis detection from chest x-ray images”. In: 2022 IEEE Calcutta Conference (CALCON).IEEE. 2022, pp. 228–232.
22. Kyeongjin Ann et al. “Generation of high-resolution chest X-rays using multi-scale conditional generative adversarial network with attention”. In: Journal of Broadcast Engineering 25.1 (2020), pp. 1–12.
23. Tomohiro Kikuchi et al. “Synthesis of hybrid data consisting of chest radiographs and tabular clinical records using dual generative models for COVID-19 positive cases”. In: Journal of Imaging Informatics in Medicine 37.3 (2024), pp. 1217–1227.
24. Laith Alzubaidi et al. “MedNet: pre-trained convolutional neural network model for the medical imaging tasks”. In: arXiv preprint arXiv:2110.06512(2021).
25. Lucas C Ribas, Wallace Casaca, and Ricardo T Fares. “Conditional Generative Adversarial Networks and Deep Learning Data Augmentation: A Multi- Perspective Data-Driven Survey Across Multiple Application Fields and Classification Architectures”. In: AI 6.2 (2025), p. 32.
26. Haruna Jallow et al. “GDP prediction of The Gambia using generative adversarial net- works”. In: Frontiers in Artificial Intelligence 8 (2025), p. 1546398.
27. Mark Sandler et al. “Mobilenetv2: Inverted residuals and linear bottlenecks”. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018, pp. 4510– 4520.
28. Gao Huang et al. “Densely connected convolutional networks”. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2017,pp. 4700–4708.
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