Attention-Guided Diagnostic Intelligence: Towards Trustworthy and Explainable Lung Disease Classification

https://doi.org/10.65770/MSPI4814

ABSTRACT

The rapid advancement of medical imaging technology has revolutionized diagnostics; however, manual interpretation remains subjective and time-intensive. This study presents an attention-based deep learning framework, the Mask-Guided Convolutional Neural Network (MG-CNN), designed to enhance both the classification accuracy and explainability of chest X-ray analysis for COVID-19 and pneumonia. Leveraging the COVID-19 Radiology Dataset, the model integrates a segmentation-based attention mechanism that dynamically prioritizes relevant pulmonary regions while suppressing background noise. Experimental results demonstrate a test accuracy of 89.97%. Crucially, the integration of Gradient-weighted Class Activation Mapping (Grad-CAM) validates that the model’s decision-making aligns with clinical pathology, focusing on lung parenchyma rather than irrelevant artifacts. This work addresses the “black box” limitation of traditional deep learning, offering a transparent, trustworthy Clinical Decision Support System (CDSS) for pulmonary medicine.

References

• [1] Alazzam, M., Alazzam, H., & Mustafa, A. (2024). Lung cancer detection using deep learning and explainable methods. Proceedings of the IEEE 25th International Conference on Circuits, Control, Communication, and Computing (14C), 1–5.
• [2] Bhattacharjee, R., Devi, S., & Sekar, K. (2022). Deep neural network based automatic detection and classification of lung nodules from CT images. Proceedings of the International Conference on Smart Computing (SMARTCON), 1–5.
• [3] Chilamkurthy, S., Ghosh, R., & Dhara, S. (2018). Deep learning based lesion detection and segmentation for automated liver MRI analysis. Medical Image Analysis, 53, 169–181. https://doi.org/10.1016/j.media.2017.08.001
• [4] Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/nature21051
• [5] Islam, R., Sumon, M., Alazzam, H., Mustafa, A., & Kim, H. (2024). Lung cancer detection using deep learning and explainable methods. Proceedings of the 25th International Conference on Circuits, Control, Communication, and Computing (14C), 1–5.
• [6] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
• [7] Li, X., Wang, Y., Monday, H. N., & Nneji, G. U. (2025). A novel residual learning of multi-scale feature extraction model for the classification of rice grain varieties. Computers and Electronics in Agriculture, 237,
• [8] Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88. https://doi.org/10.1016/j.media.2016.08.008
• [9] Meeradevi, M., Mundada, M., Singh, S. P., Bhatn, S., & Srivastava, C. (2024). Performance analysis of various deep models in lung cancer detection and classification using medical images. Proceedings of the IEEE 20th International Conference on Circuits, Control, Communication, and Computing (14C), 1–5.
• Miao, S. (2019). A survey on convolutional neural networks for image classification. Information, 10(2), https://doi.org/10.3390/information100201-C008
• Monday, H. N., Li, J., Nneji, G. U., Hossin, M. A., Nahar, S., Jackson, J., & Chikwendu, I. A. (2022). WMR-DepthwiseNet: A wavelet multi-resolution depthwise separable convolutional neural network for COVID-19 diagnosis. Diagnostics, 12(3), 765. https://doi.org/10.3390/diagnostics12030765
• Monday, H. N., Li, J., Nneji, G. U., Hossin, M. A., Nahar, S., Jackson, J., & Ejiyi, C. J. (2022). COVID-19 diagnosis from chest X-ray images using a robust multi-resolution analysis Siamese neural network with super-resolution convolutional neural network. Diagnostics, 12(3), https://doi.org/10.3390/DIAGNOSTICS12030741
• Monday, H. N., Li, J., Nneji, G. U., Ukwuoma, C. C., Cai, J., Chikwendu, I., & Oluwasanmi, A. (2022). A wavelet convolutional capsule network with modified super resolution generative adversarial network for fault diagnosis and classification. Complex & Intelligent Systems, 8(1), 1–15. https://doi.org/10.1007/s40747-022-00733-6
• Monday, H. N., Nneji, G. U., Hossin, M. A., Mark, K. D., Umana, E. S., Mgbejime, G. T., & Li, J. (2025). Enhancing ECG classification in cardiac diagnostics: A novel approach using adaptive focal cross-entropy loss function. IEEE Journal of Biomedical and Health Informatics.
• Nneji, G. U., Cai, J., Deng, J., Hossin, M. A., Nahar, S., & Jackson, J. (2022). Identification of diabetic retinopathy using weighted fusion deep learning based on dual-channel fundus scans. Diagnostics, 12(2), https://doi.org/10.3390/diagnostics12020540
• Nneji, G. U., Cai, J., Deng, J., Monday, H. N., James, E. C., & Ukwuoma, C. C. (2022). Multi-channel based image processing scheme for pneumonia identification. Diagnostics, 12(2), https://doi.org/10.3390/diagnostics12020325
• Nneji, G. U., Cai, J., Monday, H. N., Hossin, M. A., Nahar, S., Jackson, J., & Deng, J. (2022). Fine-tuned Siamese network with modified enhanced super-resolution GAN plus based on low quality chest X-ray images for COVID-19 identification. Diagnostics, 12(3), 717. https://doi.org/10.3390/diagnostics12030717
• Nneji, G. U., Deng, J., Monday, H. N., Cai, J., Hossin, M. A., Nahar, S., & Jackson, J. (2022). COVID-19 identification from low-quality computed tomography using a modified enhanced super-resolution generative adversarial network plus and Siamese capsule network. Healthcare, 10(2), https://doi.org/10.3390/healthcare10020403
• Nneji, G. U., Monday, H. N., Pathapati, V. S. R., Nahar, S., Mgbejime, G. T., Umana, E. S., & Hossin, M. A. (2025). FFS-IML: Fusion-based statistical feature selection for machine learning-driven interpretability of chronic kidney disease. International Journal of Machine Learning and Cybernetics, 1–34.
• Rajkomar, A., Hardt, M., Howell, M. D., Corrado, G., & Chin, M. H. (2018). Ensuring fairness in machine learning–driven health care: The case for algorithmic audit trails. Annals of Internal Medicine, 169(3), 205–207. https://doi.org/10.7326/annals.000-2018-0003
• Siegel, R. L., Miller, K. D., & Jemal, A. (2023). Cancer statistics, 2023. CA: A Cancer Journal for Clinicians, 73(1), 5–32. https://doi.org/10.3322/ca.23.1
• Wang, H., & Xing, L. (2021). Deep learning’s application on radiology and pathological image of lung cancer: A review. Proceedings of the International Conference on Information Technology and Biomedical Engineering (ICiTBE), 1–5.

Download all article in PDF

WSN 213 (2026) 149-169

ADVERTISEMENT