IRO Journals
Journal of Innovative Image Processing

CNN-LSTM Model for Effective Glaucoma Detection from Fundus Images

Open Access
Published: 11 July, 2026
Pages: 894-915
  • Raghda M. Nasr , Raghda M. Nasr
    Computers and Information Systems Department, Sadat Academy for Management Sciences, Cairo, Egypt
    Computers and Information Systems Department, Sadat Academy for Management Sciences, Cairo, Egypt
  • Manal A. Abdel-Fattah , Manal A. Abdel-Fattah
    Faculty of Computer Science and Artificial Intelligence, Capital University (formerly Helwan University), Cairo, Egypt
    Faculty of Computer Science and Artificial Intelligence, Capital University (formerly Helwan University), Cairo, Egypt
  • Ahmed Samir Montasser Ahmed Samir Montasser
    Faculty of Medicine, Capital University (formerly Helwan University), Cairo, Egypt
    Faculty of Medicine, Capital University (formerly Helwan University), Cairo, Egypt
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How to Cite

Nasr, Raghda M., Manal A. Abdel-Fattah, and Ahmed Samir Montasser. 2026. “CNN-LSTM Model for Effective Glaucoma Detection from Fundus Images”. Journal of Innovative Image Processing 8 (3): 894-915. https://doi.org/10.36548/jiip.2026.3.008.
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Keywords

Image classification
Deep learning
Convolutional Neural Networks (CNNs)
Long Short-Term Memory (LSTM)
Glaucoma Detection

Abstract

Glaucoma is one of the main causes of irreversible blindness, where delayed detection may increase the risk of permanent damage to the optic nerve. Developing accurate and computationally efficient computer-aided diagnostic systems using low-cost retinal fundus images remains challenging, especially with multi-disease datasets and limited clinical metadata. This paper proposes a hybrid CNN-LSTM multi-model for detecting glaucoma and six others ophthalmic diseases by merging retinal fundus images with clinically annotated patient symptoms. The model was trained and evaluated using the ODIR-5K dataset and expert-guided symptom annotation. A lightweight convolutional neural network was employed to extract visual features, while a Long Short-Term Memory network processed textual symptom descriptions. The extracted features were fused for classification across seven classes. The model achieved an accuracy of 92%, a macro-average F1-score of 74.7%, a weighted-average F1-score of 92%, and a macro-averaged AUC of 96.8% across seven predicted classes. Then, Explainable AI (XAI) techniques were applied to verify that diagnostic decisions were mainly driven by clinically relevant retinal regions. These results reveal that integrating visual and symptom-based information can improve retinal disease classification while maintaining computational efficiency, making the proposed framework suitable for deployment in limited-resource environments.

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