A Hybrid VGG-16 and TabNet Model for Interpretable Lung Disease Detection from Chest X-rays in Resource-Constrained Environments
DOI:
https://doi.org/10.33736/jcsi.10348.2026Keywords:
TabNet, VGG-16, Lung Disease Detection, Chest X-ray, Deep learning, Sequential AttentionAbstract
Accurate diagnosis of lung diseases via chest X-rays remains challenging due to subtle pathological patterns, class imbalance, and the opacity of conventional deep learning models. While convolutional neural networks excel in feature extraction, their "black-box" nature and poor interpretability hinder clinical trust, particularly in resource-constrained settings. To address these limitations, we propose a novel hybrid architecture integrating VGG-16 with TabNet, synergizing hierarchical spatial feature extraction with attention-driven interpretability. The model leverages VGG-16’s convolutional layers to capture granular details, while TabNet’s sequential attention masks dynamically prioritize discriminative features, quantifying their clinical relevance. Trained on a dataset of 2,590 chest X-rays (COPD, tuberculosis, pneumonia, and normal cases) from Nigerian hospitals, the model achieved state-of-the-art performance with 97% accuracy, surpassing ResNet-50 (95.7%) and standalone VGG-16 (94.7%). Preprocessing, including non-local means denoising and targeted augmentation, mitigates noise and class imbalance, yielding F1-scores exceeding 97% for COPD and pneumonia, with AUC values above 0.98 across all classes. The model’s interpretability is validated through attention maps highlighting disease-specific radiological markers, such as hyperinflation in COPD and consolidations in pneumonia, aligning with clinical expertise. Deployed as a real-time Android application optimized for low-end devices, the solution achieves inference in <1 second offline, addressing infrastructural barriers in low-resource regions. The model advances equitable healthcare delivery, demonstrating generalizability across demographic subgroups (accuracy deviation ≤1.2%) and compliance with emerging regulatory standards for trustworthy AI. This innovation establishes a scalable paradigm for interpretable, high-performance lung disease detection, with transformative potential for global health equity.
References
Abe, A. A., & Nyathi, M. (2025). Lung Cancer Diagnosis From Computed Tomography Images Using Deep Learning Algorithms With Random Pixel Swap Data Augmentation: Algorithm Development and Validation Study. JMIR Bioinformatics and Biotechnology, 6(1), e68848. doi: 10.2196/68848
Ahmad, M., Usman, S., Batyrshin, I., Muzammil, M., Sajid, K., Hasnain, M., & Sidorov, G. (2025). Automated diagnosis of lung diseases using vision transformer: a comparative study on chest x-ray classification. arXiv preprint arXiv:2503.18973.
Al Achkar, Z., & Chaaban, T. (2025). Palliative care for chronic respiratory diseases in low-and middle-income countries: a narrative review. Therapeutic Advances in Respiratory Disease, 19, 17534666251318616. https://doi.org/10.1177/17534666251318616
Al-Sheikh, M. H., Al Dandan, O., Al-Shamayleh, A. S., Jalab, H. A., & Ibrahim, R. W. (2023). Multi-class deep learning architecture for classifying lung diseases from chest X-ray and CT images. Scientific Reports, 13(1), 19373. https://doi.org/10.1038/s41598-023-46147-3
Alshmrani, G. M. M., Ni, Q., Jiang, R., Pervaiz, H., & Elshennawy, N. M. (2023). A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images. Alexandria Engineering Journal, 64, 923–935. https://doi.org/10.1016/j.aej.2022.10.053
Arik, S. Ö., & Pfister, T. (2021). TabNet: Attentive interpretable tabular learning. Proceedings of the AAAI Conference on Artificial Intelligence, 35(8), 6679–6687. https://doi.org/10.1609/aaai.v35i8.16826
Aslan, E. (2024). Diagnosis of Pneumonia from Chest X-ray Images with Vision Transformer Approach. Gazi University Journal of Science Part A: Engineering and Innovation, 11(2), 324-334. https://doi.org/10.54287/gujsa.1464311
Bharati, S., Podder, P., & Mondal, M. R. H. (2020). Hybrid deep learning for detecting lung diseases from X-ray images. Informatics in Medicine Unlocked, 20, 100391. https://doi.org/10.1016/j.imu.2020.100391
Bharati, S., Podder, P., Mondal, R., Mahmood, A., & Raihan-Al-Masud, M. (2020). Comparative performance analysis of different classification algorithm for the purpose of prediction of lung cancer. Advances in Intelligent Systems and Computing, 941, 447–457. https://doi.org/10.1007/978-3-030-16660-1_44
Choudhuri, R., & Paul, A. (2021, March). Multi class image classification for detection of diseases using chest X-ray images. In 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom). https://doi.org/10.1109/INDIACom51348.2021.00137
Chunli, Q., Demin, Y., Yonghong, S., & Zhijian, S. (2018). Computer aided detection in chest radiography based on artificial intelligence: A survey. BioMedical Engineering OnLine. https://doi.org/10.1186/s12938-018-0544-y
Colin, J., & Surantha, N. (2025). Interpretable Deep Learning for Pneumonia Detection Using Chest X-Ray Images. Information, 16(1), 53. https://doi.org/10.3390/info16010053
Desalu, O. O., Oluwafemi, J. A., & Ojo, O. (2009). Respiratory diseases morbidity and mortality among adults attending a tertiary hospital in Nigeria. Jornal Brasileiro de Pneumologia, 35(8), 745–752. https://doi.org/10.1590/S1806-37132009000800005
Ganeshkumar, M., Ravi, V., & Sowmya, V. (2023). Two-stage deep learning model for automate detection and classification of lung diseases. Soft Computing, 27, 15563–15579. https://doi.org/10.1007/s00500-023-09167-9
Gefter, W. B., Post, B. A., & Hatabu, H. (2023). Commonly missed findings on chest radiographs: causes and consequences. Chest, 163(3), 650-661. https://doi.org/10.1016/j.chest.2022.10.039
González, G., Ash, S. Y., Vegas-Sánchez-Ferrero, G., Onieva, J., Rahaghi, F. N., & Ross, J. C. (2018). Disease staging and prognosis in smokers using deep learning in chest computed tomography. American Journal of Respiratory and Critical Care Medicine, 197(2), 193–203. https://doi.org/10.1164/rccm.201705-0860OC
Irhebor, G. E. (2021). Respiratory health in Africa: Strides and challenges. Journal of the Pan African Thoracic Society, 2(1), 11–17. doi: 10.25259/JPATS_30_2020
Jiang, Y., Ebrahimpour, L., Després, P., & Manem, V. S. (2025). A benchmark of deep learning approaches to predict lung cancer risk using national lung screening trial cohort. Scientific Reports, 15(1), 1736. https://doi.org/10.1038/s41598-024-84193-7
Kieu, S. T. H., Bade, A., Hijazi, M. H. A., & Kolivand, H. (2020). A survey of deep learning for lung disease detection on medical images: State-of-the-art, taxonomy, issues and future directions. Journal of Imaging, 6(12), 131. https://doi.org/10.3390/jimaging6120131
Kim, S., Rim, B., Choi, S., Lee, A., Min, S., & Hong, M. (2022). Deep learning in multi-class lung diseases’ classification on chest X-ray images. Diagnostics, 12, 915. https://doi.org/10.3390/diagnostics12040915
Ko, J., Park, S., & Woo, H. G. (2024). Optimization of vision transformer-based detection of lung diseases from chest X-ray images. BMC Medical Informatics and Decision Making, 24(1), 191. https://doi.org/10.1186/s12911-024-02591-3
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Liu, T., Zhu, D., Wang, F., Rekik, I., Hu, X., & Shen, D. (2024). Editorial Special Issue on Explainable and Generalizable Deep Learning for Medical Imaging. IEEE Transactions on Neural Networks and Learning Systems, 35(6), 7271-7274. doi: 10.1109/TNNLS.2024.3395937
Martins, A., & Astudillo, R. (2016). From softmax to sparsemax: A sparse model of attention and multi-label classification. Proceedings of The 33rd International Conference on Machine Learning. In Proceedings of Machine Learning Research, 48 (pp. 1614–1623). Available from https://proceedings.mlr.press/v48/martins16.html.
Ming, J. T. C., Noor, N. M., Rijal, O. M., Kassim, R. M., & Yunus, A. (2018, July). Lung disease classification using different deep learning architectures and principal component analysis. In 2018 2nd International Conference on BioSignal Analysis, Processing and Systems (ICBAPS) (pp. 187–190).
Mingliang, X., Pei, L., Mingyuan, L., Hao, F., Hongling, Z., & Bing, Z. (2016). Medical image denoising by parallel non-local means. Neurocomputing, 195, 117–122. https://doi.org/10.1016/j.neucom.2015.08.117
Mondal, M. R. H., Bharati, S., & Podder, P. (2020). Data analytics for novel coronavirus disease. Informatics in Medicine Unlocked, 20, 100374. https://doi.org/10.1016/j.imu.2020.100374
Musa, M. (2024). MRI-Based Brain Tumor Classification using ResNet-50 and Optimized Softmax Regression. JURNAL INFOTEL, 16(3), 598-614. https://doi.org/10.20895/infotel.v16i3.1175
Olayiwola, J. O., Badejo, J. A., Okokpujie, K., & Awomoyi, M. E. (2023). Lung-related diseases classification using deep convolutional neural network. Mathematical Modelling of Engineering Problems, 10(4). https://doi.org/10.18280/mmep.100401
Ragab, M., Albukhari, A., Alyami, J., & Mansour, R. F. (2022). Ensemble deep-learning-enabled clinical decision support system for breast cancer diagnosis and classification on ultrasound images. Biology, 11(3), 439. https://doi.org/10.3390/biology11030439
Rahman, T., Khandakar, A., Kadir, M. A., Islam, K. R., Islam, K. F., & Mazhar, R. (2020). Reliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization. IEEE Access, 8, 191586–191601. doi: 10.1109/ACCESS.2020.3031384.
Rajagopal, R. K. P. M. T. K. R., Karthick, R., Meenalochini, P., & Kalaichelvi, T. (2023). Deep Convolutional Spiking Neural Network optimized with Arithmetic optimization algorithm for lung disease detection using chest X-ray images. Biomedical Signal Processing and Control, 79, 104197. https://doi.org/10.1016/j.bspc.2022.104197
Rehman, A., Khan, A., Fatima, G., Naz, S., & Razzak, I. (2023). Review on chest pathogies detection systems using deep learning techniques. Artificial Intelligence Review, 56(11), 12607-12653. https://doi.org/10.1007/s10462-023-10457-9
Shah, C., Du, Q., & Xu, Y. (2022). Enhanced TabNet: Attentive interpretable tabular learning for hyperspectral image classification. Remote Sensing, 14(3), 716. https://doi.org/10.3390/rs14030716
Shakeel, P. M., Burhanuddin, M. A., & Desa, M. I. (2019). Lung cancer detection from CT image using improved profuse clustering and deep learning instantaneously trained neural networks. Measurement, 145, 702–712. https://doi.org/10.1016/j.measurement.2019.05.027
Shukla, V., Singh, P., & Waoo, A. A. (2024). Classification of lung diseases through chest X-ray images, employing advanced deep learning techniques and explainable artificial intelligence. JETIR, 11(1).
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint. https://arxiv.org/abs/1409.1556
Sriporn, K., Tsai, C. F., Tsai, C. E., & Wang, P. (2020). Analyzing lung disease using highly effective deep learning techniques. Healthcare, 8(2), 107. https://doi.org/10.3390/healthcare8020107
Tariq, Z., Shah, S. K., & Lee, Y. (2019, November). Lung disease classification using deep convolutional neural network. In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 732–735). DOI: 10.1109/BIBM47256.2019.8983071
Van Ginneken, B., Hogeweg, L., & Prokop, M. (2009). Computer-aided diagnosis in chest radiography: Beyond nodules. European Journal of Radiology, 72(2), 226–230. https://doi.org/10.1016/j.ejrad.2009.05.061
Zakirov, A. N., Kuleev, R. F., Timoshenko, A. S., & Vladimirov, A. V. (2015). Advanced approaches to computer-aided detection of thoracic diseases on chest X-rays. Applied Mathematical Sciences, 9(88), 4361–4369. https://doi.org/10.12988/ams.2015.54348
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Journal of Computing and Social Informatics
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright Transfer Statement for Journal
1) In signing this statement, the author(s) grant UNIMAS Publisher an exclusive license to publish their original research papers. The author(s) also grant UNIMAS Publisher permission to reproduce, recreate, translate, extract or summarise, and to distribute and display in any forms, formats, and media. The author(s) can reuse their papers in their future printed work without first requiring permission from UNIMAS Publisher, provided that the author(s) acknowledge and reference publication in the Journal.
2) For open access articles, the author(s) agree that their articles published under UNIMAS Publisher are distributed under the terms of the CC-BY-NC-SA (Creative Commons Attribution-Non Commercial-Share Alike 4.0 International License) which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original work of the author(s) is properly cited.
3) For subscription articles, the author(s) agree that UNIMAS Publisher holds copyright, or an exclusive license to publish. Readers or users may view, download, print, and copy the content, for academic purposes, subject to the following conditions of use: (a) any reuse of materials is subject to permission from UNIMAS Publisher; (b) archived materials may only be used for academic research; (c) archived materials may not be used for commercial purposes, which include but not limited to monetary compensation by means of sale, resale, license, transfer of copyright, loan, etc.; and (d) archived materials may not be re-published in any part, either in print or online.
4) The author(s) is/are responsible to ensure his or her or their submitted work is original and does not infringe any existing copyright, trademark, patent, statutory right, or propriety right of others. Corresponding author(s) has (have) obtained permission from all co-authors prior to submission to the journal. Upon submission of the manuscript, the author(s) agree that no similar work has been or will be submitted or published elsewhere in any language. If submitted manuscript includes materials from others, the authors have obtained the permission from the copyright owners.
5) In signing this statement, the author(s) declare(s) that the researches in which they have conducted are in compliance with the current laws of the respective country and UNIMAS Journal Publication Ethics Policy. Any experimentation or research involving human or the use of animal samples must obtain approval from Human or Animal Ethics Committee in their respective institutions. The author(s) agree and understand that UNIMAS Publisher is not responsible for any compensational claims or failure caused by the author(s) in fulfilling the above-mentioned requirements. The author(s) must accept the responsibility for releasing their materials upon request by Chief Editor or UNIMAS Publisher.
6) The author(s) should have participated sufficiently in the work and ensured the appropriateness of the content of the article. The author(s) should also agree that he or she has no commercial attachments (e.g. patent or license arrangement, equity interest, consultancies, etc.) that might pose any conflict of interest with the submitted manuscript. The author(s) also agree to make any relevant materials and data available upon request by the editor or UNIMAS Publisher.
