Automated Eye Disease Diagnosis Using a 2D CNN with Grad-CAM: High-Accuracy Detection of Retinal Asymmetries for Multiclass Classification

• Published in: Symmetry (Basel) Vol. 17; no. 5; p. 768
• Main Authors: Abd El-Ghany, Sameh, Mahmood, Mahmood A., Abd El-Aziz, A. A.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2025
• Subjects: Accuracy; Artificial neural networks; Asymmetry; Blindness; Cataracts; Classification; Development and progression; Diabetes therapy; Diabetic retinopathy; Diagnostic systems; Empowerment; Eye diseases; Glaucoma; Macular degeneration; Medical imaging equipment; Vision; Visual impairment; Workflow
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym17050768

Eye diseases (EDs), including glaucoma, diabetic retinopathy, and cataracts, are major contributors to vision loss and reduced quality of life worldwide. These conditions not only affect millions of individuals but also impose a significant burden on global healthcare systems. As the population ages and lifestyle changes increase the prevalence of conditions like diabetes, the incidence of EDs is expected to rise, further straining diagnostic and treatment resources. Timely and accurate diagnosis is critical for effective management and prevention of vision loss, as early intervention can significantly slow disease progression and improve patient outcomes. However, traditional diagnostic methods rely heavily on manual analysis of fundus imaging, which is labor-intensive, time-consuming, and subject to human error. This underscores the urgent need for automated, efficient, and accurate diagnostic systems that can handle the growing demand while maintaining high diagnostic standards. Current approaches, while advancing, still face challenges such as inefficiency, susceptibility to errors, and limited ability to detect subtle retinal asymmetries, which are critical early indicators of disease. Effective solutions must address these issues while ensuring high accuracy, interpretability, and scalability. This research introduces a 2D single-channel convolutional neural network (CNN) based on ResNet101-V2 architecture. The model integrates gradient-weighted class activation mapping (Grad-CAM) to highlight retinal asymmetries linked to EDs, thereby enhancing interpretability and detection precision. Evaluated on retinal Optical Coherence Tomography (OCT) datasets for multiclass classification tasks, the model demonstrated exceptional performance, achieving accuracy rates of 99.90% for four-class tasks and 99.27% for eight-class tasks. By leveraging patterns of retinal symmetry and asymmetry, the proposed model improves early detection and simplifies the diagnostic workflow, offering a promising advancement in the field of automated eye disease diagnosis.