Hybrid Neural Architectures Combining Convolutional and Recurrent Networks for the Early Detection of Retinal Pathologies
Mamyrbayev O. Pavlov S. Poplavskyi O. Momynzhanova K. Saldan Y. Zhanegiz A. Zhumagulova S. Zhumazhan N.
August 2025Dr D. Pylarinos
Engineering, Technology and Applied Science Research
2025#15Issue 425150 - 25157 pp.
Early and accurate detection of retinal pathologies is critical for preventing vision loss and enabling timely clinical intervention. Traditional computer vision techniques, such as thresholding, edge detection, morphological filtering, and Hough transforms, have long been used to extract features from retinal fundus images, yet their performance is often constrained by image variability and complex pathological presentations. This study presents a hybrid deep learning architecture that integrates Convolutional Neural Networks (CNNs) for image-based classification with Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) units, to model geometric and anatomical features derived from classical methods. This architecture allows for the fusion of pixel-level deep features with clinically interpretable descriptors, including optic disc-fovea distance, lesion spatial distribution, and vessel curvature sequences. Comparative analysis demonstrates that the proposed hybrid model achieves superior diagnostic accuracy, reaching 97%, significantly outperforming both conventional image processing approaches and CNN-only baselines. The results indicate that incorporating structured domain knowledge into neural models improves both performance and interpretability, offering a robust framework for real-world retinal disease screening applications.
convolutional neural networks , deep learning , fundus imaging , medical image classification , optic disc localization , recurrent neural networks , retinal pathology detection , vessel analysis
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Laboratory of Computer Engineering of Intelligent Systems, Institute of Information and Computational Technologies, Almaty, Kazakhstan
Scientific Laboratory of Biomedical Optics and Photonics, Department of Biomedical Engineering and Department of Laser and Optoelectronic Engineering, Vinnytsia National Technical University, Vinnytsia, Ukraine
Kyiv National University of Construction and Architecture, Kyiv, Ukraine
Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty, Kazakhstan
Eye Diseases and Eye Microsurgery Department, National Pirogov Memorial Medical University, Vinnytsia, Ukraine
U. Joldasbekov Institute of Mechanics and Engineering, Almaty, Kazakhstan
Laboratory of Computer Engineering of Intelligent Systems
Scientific Laboratory of Biomedical Optics and Photonics
Kyiv National University of Construction and Architecture
Faculty of Information Technology
Eye Diseases and Eye Microsurgery Department
U. Joldasbekov Institute of Mechanics and Engineering
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