Deep Learning-Based Interpretable Framework for Simultaneous Segmentation of Retinal Multi-lesions and Multi-categorical Disease Classification

This paper addresses the limitations of existing deep learning techniques in retinal disease diagnosis from fundus images, specifically focusing on inadequate feature representation, localization challenges, and the lack of interpretability. Current methods also struggle with class imbalance and the need for simultaneous multi-task learning. To overcome these issues, we propose a novel interpretable deep learning framework for the simultaneous segmentation of multiple retinal lesions and multi-categorical disease classification with grading. Our framework incorporates a modified Convolutional Neural Network (CNN) with separable convolution layers for efficient parameter utilization and an attention mechanism to focus on critical image regions, enabling accurate lesion mapping. To tackle class imbalance, the framework utilizes the Synthetic Minority Over-Sampling Technique (SMOTE). Furthermore, Explainable AI (XAI) techniques, including Grad-Cam, are integrated to enhance the model's interpretability and provide insights into its decision-making process. The effectiveness of the proposed framework is evaluated on several publicly available datasets, including ODIR, RFMiD, and IDRiD. The framework aims to provide a comprehensive, balanced, and interpretable solution for real-time fundus disease diagnosis in healthcare. The proposed architecture outperforms existing models in accuracy, AUC, mAUPR, and mIoU on retinal fundus image datasets. Heatmaps and segmentation masks confirm their effectiveness in identifying disease regions and segmenting lesions.