Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 10.54216/JAIM https://www.americaspg.com/journals/show/4116 2022 2022 Department of Chemistry and Biochemistry, University of Arkansas, Fayetteville, AR 72701, USA Ehsan Ehsan School of Engineering and Technology, Amity University Kolkata, Kolkata, India P. K. Dutta Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia Amel Ali Alhussan Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA; Jadara Research Center, Jadara University, Irbid 21110, Jordan Marawa Metwally The ability to accurately classify retinal fundus images has been made possible by rapid improvements in deep learning (DL) and artificial intelligence (AI). This motivation led to developing a new AI-driven hybrid Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) architecture for precisely categorizing retinal diseases. The model first receives high-resolution retinal fundus images to extract various spatial properties, which are then processed by two parallel CNN branches after a standard convolutional layer. These branches use residual learning with convolutional and identity blocks to extract features. Following the reshaping and concatenation of the features from both branches, an LSTM layer captures inter-feature relationships. Eight retinal disorders are then predicted to belong to the same disease class via a fully linked classifier. Extensive simulations were run on a benchmark retinal OCT dataset, and performance was assessed using various criteria. The experimental results showed that the suggested hybrid model was adequate, with a high overall accuracy of 93% with F1-score values of 0.93, 0.94, and 0.93 for precision, recall, and accuracy, respectively. The model demonstrated considerable predictive abilities for all classes while perfectly classifying AMD, CNV, CSR, DME, DR, MH, and routine diseases to reveal its clinical value as an automated retinal processor. 2025 2025 01 19 10.54216/JAIM.100201 https://www.americaspg.com/articleinfo/28/show/4116