11th International Conference on ICTIS

Authors - Subin Simon, Prathilothamai M
Abstract - Deep learning has shown significant potential in medical image classification; however, a systematic comparison of deep feature extraction strategies for multi class diabetic eye disease assessment remains limited. This study presents a comprehensive comparative analysis of seven deep learning architectures, including conventional CNN, pretrained VGG16, Vision Transformer (ViT), Conformer, hybrid CNN ViT, and attention-augmented variants incorporating Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM). All models are evaluated under a unified preprocessing and training framework to ensure fair performance comparison.The investigation focuses on analyzing how different architectural paradigms capture discriminative local and global retinal features relevant to disease classification. Extensive experiments are conducted on public fundus image datasets using standard evaluation metrics, including accuracy, precision, recall, and F1-score. Experimental results demonstrate that hybrid and attention-integrated architectures outperform standalone CNN and transformer models. In particular, the Conformer architecture achieves the best overall performance, reaching approximately 91% classification accuracy in the four class setting (Diabetic Retinopathy, Glaucoma, Cataract, and Normal), while the CNN ViT model attains approximately 89% accuracy.These findings highlight the effectiveness of combining convolutional operations with global self-attention mechanisms for robust and discriminative feature extraction in automated diabetic eye disease classification.