This paper presents a deep learning-based intelligent web application for the early detection of eye diseases using retinal fundus images. The dataset used in this study consists of 4,216 retinal fundus images collected from Kaggle, representing multiple eye disease categories. Multiple deep learning architectures, including CNN, DenseNet, InceptionV3, and ResNet, were evaluated and compared with a proposed modified MobileNetV2 architecture. The proposed architecture enhances the baseline MobileNetV2 by optimizing feature extraction and classification layers for improved performance in multi-class eye disease detection. Experimental results show that the proposed model achieved an overall cl... More >