aspg-28-4116-1792155141 20261016125221 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 2025 10 2 Ehsan Khodadadi Department of Chemistry and Biochemistry, University of Arkansas, Fayetteville, AR 72701, USA P. K. Dutta School of Engineering and Technology, Amity University Kolkata, Kolkata, India Amel Ali Alhussan Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia Marawa Metwally Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA; Jadara Research Center, Jadara University, Irbid 21110, Jordan Optical coherence tomography (OCT) provides detailed retinal images, but their interpretation can be difficult across clinical settings and patient groups. This paper presents a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture for classifying retinal OCT images into eight disease categories. Two parallel convolutional branches extract spatial features; their outputs are pooled, reshaped into a sequence, and processed by an LSTM layer to model inter-feature relationships before dense classification. The model is evaluated on 24,000 images and compared with ResNet50, MobileNetV2, InceptionV3, and Xception. The proposed architecture achieves 93.00% accuracy, 94% precision, 93% recall, and a 0.93 F1-score, outperforming the compared models. The results indicate that combining convolutional feature extraction with sequence modeling is a promising approach to automated retinal disease classification. 2025 01 08 4116 https://creativecommons.org/licenses/by/4.0/ 10.54216/JAIM.100201 https://www.americaspg.com/journal/28/article/4116 https://www.americaspg.com/storage/articles/manuscripts/01M4J4ENDWD4JC2M34JXBV4JJM.pdf