aspg-28-4111-1792155110 20261016125150 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 2025 10 1 Nima Khodadadia Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA Benyamin Abdollahzadeha Department of Mathematics, Faculty of Science, University of Hradec Králové, 500 03, Hradec Králové, Czech Republic Retinal diseases are a major cause of vision loss, making timely and accurate diagnosis important for effective treatment. This study compares convolutional neural networks (CNN), k-nearest neighbors (K-NN), and support vector machines (SVM) for classifying digital fundus images into four categories: normal retina, cataract, glaucoma, and other retinal diseases. The models are evaluated using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F-score. CNN achieves the highest reported accuracy (94.05%) and sensitivity (0.9977), with an F-score of 0.9693. The results indicate that CNN can be useful for automated retinal screening, while its low specificity (0.0178) highlights the need to reduce false-positive predictions. The comparison illustrates both the potential and the limitations of machine-learning-based retinal disease classification. 2025 01 07 4111 https://creativecommons.org/licenses/by/4.0/ 10.54216/JAIM.100101 https://www.americaspg.com/journal/28/article/4111 https://www.americaspg.com/storage/articles/manuscripts/01M4J2VYG6D1G4M9C030WQ851F.pdf