Automated retinal disease classification using deep learning and AlexNet with statistical models analysis.

ABSTRACT: Diabetic Retinopathy, Cataract, and Glaucoma are major retinal diseases that require early detection to prevent irreversible vision loss. This study proposes a deep learning-based framework for the automated classification of retinal images into four categories: Normal, Diabetic Retinopathy, Cataract, and Glaucoma. The dataset was compiled from publicly available retinal imaging databases, including IDRiD and HRF. Four convolutional neural network architectures-EfficientNet-B0, EfficientNet-B7, a build-from-scratch model, and AlexNet-were evaluated using multiple performance metrics. Among these, AlexNet achieved the highest overall performance, attaining an accuracy of 93.65%, sensitivity of 94.39%, specificity of 98.05%, PPV of 93.65%, NPV of 97.95%, and an F1-score of 93.74%. EfficientN

SUBMITTER: Elkenawy EM 

PROVIDER: S-EPMC12774357 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature