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Original Article
Explainable AI-Based Pneumonia Detection System Using Efficientnetb0, SVM, And Grad-Cam Visualization
Dr.A.Senthil Kumar1
Reena.R2
Rakshika.C3
Shafia Kulsoom.S4
1 Professor , Department of Electrical Electronics and Engineering, Er. Perumal Manimekalai College of Engineering, Hosur, Tamilnadu, India. 2 3 4 Final Year, Department of Information Technology, Er. Perumal Manimekalai College of Engineering, Hosur, Tamilnadu, India.
Published Online: March-April 2026
Pages: 449-457
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260702053
References
1. World Health Organization (WHO), “Pneumonia Fact Sheet,” 2022. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/ pneumonia
2. M. Tan and Q. V. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” in Proc. 36th Int. Conf. Mach. Learn. (ICML), Long Beach, CA, PMLR 97, 2019, pp. 6105–6114.
3. R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization,” in IEEE Int. Conf. Comput. Vis. (ICCV), Venice, Italy, 2017, pp. 618–626.
4. C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn., vol. 20, no. 3, pp. 273–297, 1995.
5. P. Rajpurkar et al., “CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning,” arXiv preprint arXiv:1711.05225, 2017.
6. D. S. Kermany et al., “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell, vol. 172, no. 5, pp. 1122– 1131.e9, 2018.
7. K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Las Vegas, NV, 2016, pp. 770–778.
8. X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, and R. M. Summers,“ChestX-ray14: Massive Chest X-ray Dataset and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases,” in IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Honolulu, HI, 2017, pp. 3462–3471.
9. K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” in Int. Conf. Learn. Represent. (ICLR), San Diego, CA, 2015.
10. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Commun. ACM, vol. 60, no. 6, pp. 84–90, 2017.
11. O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. (IJCV), vol. 115, no. 3, pp. 211–252, 2015.
12. F. Chollet, “Xception: Deep Learning with Depthwise Separable Convolutions,” in IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Honolulu, HI, 2017, pp. 1800–1807.
13. J. Platt, “Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods,” Adv. Large Margin Classif., vol. 10, no. 3, pp. 61–74, 1999.
14. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
15. Streamlit Inc., “Streamlit Documentation,” 2024. [Online]. Available: https://docs.streamlit.io/
16. Q. An, W. Chen, and W. Shao, “A Deep Convolutional Neural Network for Pneumonia Detection in X-ray Images with Attention Ensemble,” Diagnostics, vol. 14, no. 4, p. 390, 2024. doi: 10.3390/diagnostics14040390
17. L. Teixeira et al., “Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey,” J. Imaging, vol. 10, no. 8, p. 176, 2024. doi: 10.3390/jimaging10080176
18. E. P. Reis et al., “Pneumonia Detection from Chest X-Ray Images Using Deep Learning and Transfer Learning for Imbalanced Datasets,” J. Digit. Imaging Inform. Med., 2024. doi: 10.1007/s10278-024-01287-8
19. V. Ravi, V. Acharya, and M. Alazab, “A Multichannel EfficientNet Deep Learning-Based Stacking Ensemble Approach for Lung Disease Detection Using Chest X-ray Images,” Cluster Comput., vol. 26, pp. 1181– 1203, 2023. doi: 10.1007/s10586-022-03664-6
20. D. Keerthana, V. Venugopal, M. K. Nath, and M. Mishra, “Hybrid Convolutional Neural Networks with SVM Classifier for Classification of Skin Cancer,” Biomed. Eng. Adv., vol. 5, p. 100069, 2023. doi: 10.1016/j.bea.2022.100069
21. O. Mansouri et al., “Hybrid CNN and SVM Model for Alzheimer’s Disease Classification Using Categorical Focal Loss Function,” Biomed. Signal Process. Control, 2025. doi: 10.1016/j.bspc.2025.000385
22. M. Togacar et al., “D3SENet: A Hybrid Deep Feature Extraction Network for Covid-19 Classification Using Chest X-ray Images,” Biomed. Signal Process. Control, vol. 82, p. 104559, 2023. doi: 10.1016/j.bspc.2022.104559
23. B. Mustapha et al., “Interpretable Deep Learning for Pneumonia Detection Using Chest X-Ray Images,” Information, vol. 16, no. 1, p. 53, 2025. doi: 10.3390/info16010053
24. S. Sharma and K. Guleria, “A Systematic Literature Review on Deep Learning Approaches for Pneumonia Detection Using Chest X-ray Images,” Multimed. Tools Appl., vol. 83, no. 8, pp. 24101–24151, 2024.
25. M. Aasem and M. J. Iqbal, “Toward Explainable AI in Radiology: Ensemble-CAM for Effective Thoracic Disease Localization in Chest Xray Images Using Weak Supervised Learning,” Front. Big Data, vol. 7, p. 1366415, 2024. doi: 10.3389/fdata.2024.1366415
26. I. E. Ihongbe, S. Fouad, T. F. Mahmoud, A. Rajasekaran, and B. Bhatia, “Evaluating Explainable Artificial Intelligence (XAI) Techniques in Chest Radiology Imaging Through a Human-Centered Lens,” PLoS ONE, vol. 19, no. 10, p. e0308758, 2024. doi: 10.1371/journal.pone.0308758
27. B. H. M. Van der Velden, H. J. Kuijf, K. G. A. Gilhuijs, and M. A. Viergever, “Explainable Artificial Intelligence (XAI) in Deep Learning-Based Medical Image Analysis,” Med. Image Anal., vol. 79, p. 102470, 2022. doi: 10.1016/j.media.2022.102470
28. D. Saeed and C. Omlin, “Explainable AI (XAI): A Systematic MetaSurvey of Current Challenges and Future Opportunities,” Knowl.-Based Syst., vol. 263, p. 110273, 2023. doi: 10.1016/j.knosys.2023.110273
29. H. Ghassemi, A. Rezapour, and V. Arabloo, “Unveiling the Black Box: A Systematic Review of Explainable Artificial Intelligence in Medical Image Analysis,” Comput. Struct. Biotechnol. J., 2024.doi: 10.1016/j.csbj.2024.05.012
30. M. T. Ribeiro, S. Singh, and C. Guestrin, “‘Why Should I Trust You?’: Explaining the Predictions of Any Classifier,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., San Francisco, CA, 2016, pp. 1135–1144.
31. S. Suara, A. Jha, P. Sinha, and A. A. Sekh, “Is Grad-CAM Explainable in Medical Images?” arXiv preprint arXiv:2307.10506, 2023.
2. M. Tan and Q. V. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” in Proc. 36th Int. Conf. Mach. Learn. (ICML), Long Beach, CA, PMLR 97, 2019, pp. 6105–6114.
3. R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization,” in IEEE Int. Conf. Comput. Vis. (ICCV), Venice, Italy, 2017, pp. 618–626.
4. C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn., vol. 20, no. 3, pp. 273–297, 1995.
5. P. Rajpurkar et al., “CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning,” arXiv preprint arXiv:1711.05225, 2017.
6. D. S. Kermany et al., “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell, vol. 172, no. 5, pp. 1122– 1131.e9, 2018.
7. K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Las Vegas, NV, 2016, pp. 770–778.
8. X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, and R. M. Summers,“ChestX-ray14: Massive Chest X-ray Dataset and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases,” in IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Honolulu, HI, 2017, pp. 3462–3471.
9. K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” in Int. Conf. Learn. Represent. (ICLR), San Diego, CA, 2015.
10. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Commun. ACM, vol. 60, no. 6, pp. 84–90, 2017.
11. O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. (IJCV), vol. 115, no. 3, pp. 211–252, 2015.
12. F. Chollet, “Xception: Deep Learning with Depthwise Separable Convolutions,” in IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Honolulu, HI, 2017, pp. 1800–1807.
13. J. Platt, “Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods,” Adv. Large Margin Classif., vol. 10, no. 3, pp. 61–74, 1999.
14. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
15. Streamlit Inc., “Streamlit Documentation,” 2024. [Online]. Available: https://docs.streamlit.io/
16. Q. An, W. Chen, and W. Shao, “A Deep Convolutional Neural Network for Pneumonia Detection in X-ray Images with Attention Ensemble,” Diagnostics, vol. 14, no. 4, p. 390, 2024. doi: 10.3390/diagnostics14040390
17. L. Teixeira et al., “Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey,” J. Imaging, vol. 10, no. 8, p. 176, 2024. doi: 10.3390/jimaging10080176
18. E. P. Reis et al., “Pneumonia Detection from Chest X-Ray Images Using Deep Learning and Transfer Learning for Imbalanced Datasets,” J. Digit. Imaging Inform. Med., 2024. doi: 10.1007/s10278-024-01287-8
19. V. Ravi, V. Acharya, and M. Alazab, “A Multichannel EfficientNet Deep Learning-Based Stacking Ensemble Approach for Lung Disease Detection Using Chest X-ray Images,” Cluster Comput., vol. 26, pp. 1181– 1203, 2023. doi: 10.1007/s10586-022-03664-6
20. D. Keerthana, V. Venugopal, M. K. Nath, and M. Mishra, “Hybrid Convolutional Neural Networks with SVM Classifier for Classification of Skin Cancer,” Biomed. Eng. Adv., vol. 5, p. 100069, 2023. doi: 10.1016/j.bea.2022.100069
21. O. Mansouri et al., “Hybrid CNN and SVM Model for Alzheimer’s Disease Classification Using Categorical Focal Loss Function,” Biomed. Signal Process. Control, 2025. doi: 10.1016/j.bspc.2025.000385
22. M. Togacar et al., “D3SENet: A Hybrid Deep Feature Extraction Network for Covid-19 Classification Using Chest X-ray Images,” Biomed. Signal Process. Control, vol. 82, p. 104559, 2023. doi: 10.1016/j.bspc.2022.104559
23. B. Mustapha et al., “Interpretable Deep Learning for Pneumonia Detection Using Chest X-Ray Images,” Information, vol. 16, no. 1, p. 53, 2025. doi: 10.3390/info16010053
24. S. Sharma and K. Guleria, “A Systematic Literature Review on Deep Learning Approaches for Pneumonia Detection Using Chest X-ray Images,” Multimed. Tools Appl., vol. 83, no. 8, pp. 24101–24151, 2024.
25. M. Aasem and M. J. Iqbal, “Toward Explainable AI in Radiology: Ensemble-CAM for Effective Thoracic Disease Localization in Chest Xray Images Using Weak Supervised Learning,” Front. Big Data, vol. 7, p. 1366415, 2024. doi: 10.3389/fdata.2024.1366415
26. I. E. Ihongbe, S. Fouad, T. F. Mahmoud, A. Rajasekaran, and B. Bhatia, “Evaluating Explainable Artificial Intelligence (XAI) Techniques in Chest Radiology Imaging Through a Human-Centered Lens,” PLoS ONE, vol. 19, no. 10, p. e0308758, 2024. doi: 10.1371/journal.pone.0308758
27. B. H. M. Van der Velden, H. J. Kuijf, K. G. A. Gilhuijs, and M. A. Viergever, “Explainable Artificial Intelligence (XAI) in Deep Learning-Based Medical Image Analysis,” Med. Image Anal., vol. 79, p. 102470, 2022. doi: 10.1016/j.media.2022.102470
28. D. Saeed and C. Omlin, “Explainable AI (XAI): A Systematic MetaSurvey of Current Challenges and Future Opportunities,” Knowl.-Based Syst., vol. 263, p. 110273, 2023. doi: 10.1016/j.knosys.2023.110273
29. H. Ghassemi, A. Rezapour, and V. Arabloo, “Unveiling the Black Box: A Systematic Review of Explainable Artificial Intelligence in Medical Image Analysis,” Comput. Struct. Biotechnol. J., 2024.doi: 10.1016/j.csbj.2024.05.012
30. M. T. Ribeiro, S. Singh, and C. Guestrin, “‘Why Should I Trust You?’: Explaining the Predictions of Any Classifier,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., San Francisco, CA, 2016, pp. 1135–1144.
31. S. Suara, A. Jha, P. Sinha, and A. A. Sekh, “Is Grad-CAM Explainable in Medical Images?” arXiv preprint arXiv:2307.10506, 2023.
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