References
Malhotra, P., Gupta, S., Koundal, D., Zaguia, A., Kaur, M., & Lee, H.-N. (2022). Deep learning- based computer-aided pneumothorax detection using chest X-ray images. Sensors, 22(6), Article 2278. https://doi.org/10.3390/s220622278 Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., Shpanskaya, K., Lungren, M. P., & Ng, A. Y. (2018). Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Medicine, 15(11), e1002686. https://doi.org/10.1371/journal.pmed.1002686 Google. (2024). EfficientNet-B2. Hugging Face. https://huggingface.co/google/efficientnet-b2 Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv. https://doi.org/10.48550/arXiv.1502.03167 Vennerød, C. B., Kjærran, A., and Bugge, E. S. (2021). Long short-term memory RNN. arXiv. https://doi.org/10.48550/arXiv.2105.06756 Jin, P., Zhu, B., Yuan, L., and Yan, S. (2024). MoH: Multi-head attention as mixture-of-head attention. arXiv. https://doi.org/10.48550/arXiv.2410.11842 Sanida T and Dasygenis M. (2024). A novel lightweight CNN for chest X-ray-based lung disease identification on heterogeneous embedded systems. Applied Intelligence, 54(6), 4756– 4780. https://doi.org/10.1007/s10489-024-05420-2 Wan G. and Yao L (2024). ‘LMFRNet: A Lightweight Convolutional Neural Network Model for Image Analysis’, Electronics, 13(1) https://doi.org/10.3390/electronics13010129. Liu Y., Xue J., Li D., Zhang W., Chiew T., and Xu Z. (2024). ‘Image recognition based on lightweight convolutional neural network: Recent advances’, Image Vis. Comput., 146, 105037, https://doi.org/10.1016/j.imavis.2024.105037. ResearchGate (2024). ‘A lightweight deep learning architecture for the automatic detection of pneumonia using chest X-ray images, https://doi.org/10.1007/s11042-021-11807-x. Asham M., Al-Shargabi A., Al-Sabri R., and Meftah I. (2024). ‘A lightweight deep learning model with knowledge distillation for pulmonary diseases detection in chest X-rays’, Multimed. Tools Appl., https://doi.org/10.1007/s11042-024-19638-2. Asif, S., & Qurrat-ul-Ain. (2024). Enhancing pulmonary abnormality detection with an optimized CNN architecture incorporating depth-wise separable convolution and inception module. Evolutionary Systems, 15(4), 1359–1380. https://doi.org/10.1007/s12530-023-09565-2 Cho, Y., Kim, J., Lim, T. H., Lee, I., & Choi, J. (2021). Detection of the location of pneumothorax in chest X-rays using small artificial neural networks and a simple training process. Scientific Reports, 11, Article 92523. https://doi.org/10.1038/s41598-021-92523-2 Chiwhane, S., Shrotriya, L., Dhumane, A., Kothari, S., Dharrao, D., & Bagane, P. (2024). Data mining approaches to pneumothorax detection: Integrating Mask R-CNN and medical transfer learning techniques. MethodsX, 12, 102692. https://doi.org/10.1016/j.mex.2024.102692 Li, X., Wang, Y., Monday, H.N. and 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, p.110491. Monday, H.N., Nneji, G.U., Hossin, M.A., Mark, K.D., Umana, E.S., Mgbejime, G.T. and 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. Vovk V. (2015). ‘The Fundamental Nature of the Log Loss Function’, in Fields of Logic and Computation II: Essays Dedicated to Yuri Gurevich on the Occasion of His 75th Birthday, L. D. Beklemishev, A. Blass, N. Dershowitz, B. Finkbeiner, and W. Schulte, Eds., Cham: Springer International Publishing, pp. 307–318. https://doi.org/10.1007/978-3-319-23534- 9_20. 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. Monday, H. N., Li, J., Nneji, G. U., Ukwuoma, C. C., Cai, J., Chikwendu, I., & Oluwasanmi, A. (2022a). A wavelet convolutional capsule network with modified super-resolution GAN for fault diagnosis and classification. Complex & Intelligent Systems, 8(1), 1–15. https://doi.org/10.1007/s40747-022-00733-6 Monday, H. N., Li, J., Nneji, G. U., Hossin, M. A., Nahar, S., Jackson, J., & Chikwendu, I. A. (2022b). WMR-DepthwiseNet: A wavelet multi-resolution depthwise separable CNN 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. (2022c). COVID-19 diagnosis from chest X-ray images using a robust multi-resolution analysis Siamese neural network. Diagnostics, 12(3), 741. https://doi.org/10.3390/diagnostics12030741 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 for COVID- 19 identification. Diagnostics, 12(3), 717. https://doi.org/10.3390/diagnostics12030717 Nneji G., Deng J., Monday H., Cai J., Hossin M., Nahar S., and 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), 403, https://doi.org/10.3390/healthcare10020403. Nneji G., Cai J., Deng M., Hossin A., Nahar S., and Jackson J. (2022). “Identification of Diabetic Retinopathy Using Weighted Fusion Deep Learning Based on Dual-Channel Fundus Scans”, Diagnostics, 12(2), 540 https://doi.org/10.3390/diagnostics12020540. Nneji G., Cai J., Deng J., Monday H., James E., and Ukwuoma C. (2022). “Multi-Channel Based Image Processing Scheme for Pneumonia Identification”, Diagnostics, 12(2),325, https://doi.org/10.3390/diagnostics12020325