Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Volume 10 , Issue 2 , PP: 01-19, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Ehsan Khodadadi 1 * , P. K. Dutta 2 , Amel Ali Alhussan 3 , Marawa Metwally 4
Doi: https://doi.org/10.54216/JAIM.100201
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.
Retinal disease classification , Deep learning , CNN-LSTM architecture , Fundus imaging , Residual learning , Optical Coherence Tomography (OCT) , Medical image analysis
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