References
AIML.com. (n.d.). What is dying ReLU or dead ReLU and why is this a problem in neural network training? https://aiml.com/what-is-dying-relu-or-dead-relu-and-why-is-this-a-problem-in- neural-network-training/ Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). SegNet: A deep convolutional encoder- decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481–2495. https://doi.org/10.1109/TPAMI.2016.2644615 Ben Romdhane, N., Mliki, H., & Hammami, M. (2016). An improved traffic signs recognition and tracking method for driver assistance system. In Proceedings of the 15th IEEE/ACIS International Conference on Computer and Information Science (pp. 1–6). https://doi.org/10.1109/ICIS.2016.7550772 Bhatt, N., Laldas, P., & Lobo, V. B. (2022). A real-time traffic sign detection and recognition system on hybrid dataset using CNN. In Proceedings of the 7th International Conference on Communication and Electronics Systems (pp. 1354–1358). https://doi.org/10.1109/ICCES54183.2022.9835954 Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., & Wang, M. (2021). Swin-Unet: Unet- like pure transformer for medical image segmentation. arXiv. https://arxiv.org/abs/2105.05537 Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation. arXiv. https://arxiv.org/abs/1803.08375 Choda, M. V. K., Perla, S. V., Shaik, B., Yelchuru, Y. T. A., & Yalla, P. (2023). A critical survey on real-time traffic sign recognition using CNN machine learning algorithm. In Proceedings of the International Conference on Intelligent Data Communication Technologies and Internet of Things (pp. 445–450). https://doi.org/10.1109/IDCIoT56793.2023.10053394 Dong, Z., Li, P., Wang, Z., Wu, Y., Wang, F., & Lu, H. (2023). A review of U-Net and its variants for medical image segmentation. Signal, Image and Video Processing, 17(8), 4071–4081. https://pmc.ncbi.nlm.nih.gov/articles/PMC9989586/ Hasegawa, R., Iwamoto, Y., & Chen, Y.-W. (2019). Robust detection and recognition of Japanese traffic sign in complex scenes based on deep learning. In Proceedings of the IEEE 8th Global Conference on Consumer Electronics (pp. 575–578). https://doi.org/10.1109/GCCE46687.2019.9015419 Janai, J., Güney, F., Behl, A., & Geiger, A. (2021). Computer vision for autonomous vehicles: Problems, datasets and state of the art. arXiv. https://doi.org/10.48550/arXiv.1704.05519 Li, C., et al. (2022). YOLOv6: A single-stage object detection framework for industrial applications. arXiv. http://arxiv.org/abs/2209.02976 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, 110491. Monday, H. N., Li, J., G. U. Nneji, C. C. Ukwuoma, J. Cai, I. Chikwendu, & A. Oluwasanmi. (2022a). 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., Li, J., Nneji, G. U., Hossin, M. A., Nahar, S., Jackson, J., & Chikwendu, I. A. (2022b). WMR-DepthwiseNet: A wavelet multi-resolution depthwise separable IIARD International Journal of Geography & Environmental Management 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. (2022c). COVID-19 diagnosis from chest X-ray images using a robust multi-resolution analysis Siamese neural network with super-resolution CNN. Diagnostics, 12(3), 741. https://doi.org/10.3390/diagnostics12030741 Monday, H. N., Nneji, G. U., Hossin, M. A., Mark, K. D., Umana, E. S., Mgbejime, G. T., & Li, J. (2025a). Enhancing ECG classification in cardiac diagnostics 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. (2022c). 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. U., Cai, J., Deng, J., Monday, H. N., James, E. C., & Ukwuoma, C. C. (2022d). Multi- channel based image processing scheme for pneumonia identification. Diagnostics, 12(2), 325. https://doi.org/10.3390/diagnostics12020325 Nneji, G. U., Deng, J., Monday, H. N., Cai, J., Hossin, M. A., Nahar, S., & Jackson, J. (2022b). COVID-19 identification from low-quality CT using a modified enhanced SRGAN plus and Siamese capsule network. Healthcare, 10(2), 403. 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. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 (pp. 234–241). Springer. https://doi.org/10.1007/978-3-319-24574-4_28 Shanmugam, R. (2024). SegNet network architecture for deep learning image segmentation and its integrated applications and prospects. https://www.researchgate.net/ Sukhani, K., Shankarmani, R., Shah, J., & Shah, K. (2021). Traffic sign board recognition and voice alert system using convolutional neural network. In Proceedings of the 2nd International Conference for Emerging Technology (pp. 1–5). https://doi.org/10.1109/INCET51464.2021.9456302