Hybrid CNN-LSTM Architecture for OCT Retinal Disease
Classification
Ehsan Khodadadi1,*, P. K. Dutta2, Amel Ali Alhussan3, Marawa Metwally4,5
1Department of Chemistry and Biochemistry, University of Arkansas, Fayetteville, AR 72701, USA
2School of Engineering and Technology, Amity University Kolkata, Kolkata, India
3Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint
Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
4Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA
5Jadara Research Center, Jadara University, Irbid 21110, Jordan
Emails: Ehsank@uark.edu; pkdutta@kol.amity.edu; aaalhussan@pnu.edu.sa; amel.alhusan@gmail.com;
marwa@jcsis.org
Abstract
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.
Keywords: Retinal disease classification; Deep learning; CNN-LSTM architecture; Fundus imaging;
Residual learning; Optical Coherence Tomography (OCT); Medical image analysis