Analysis of machine learning methods for detection of cataracts
Tyunina A. Rakhmetulayeva S. Schiller E.
February 2026Institute of Advanced Engineering and Science
Bulletin of Electrical Engineering and Informatics
2026#15Issue 1406 - 423 pp.
Cataracts remain the leading cause of visual impairment worldwide. We focus on improving the you only look once (YOLO) architecture through targeted optimization to enhance feature extraction. We trained the optimized YOLOv8 detector using 11,274 annotated fundus and anterior segment images. During training, five-fold cross-validation, color magnification, and stochastic weight averaging (SWA) were applied to ensure convergence. In the external test set, the model achieved an F1-score of 98.9% and an mAP50 of 0.995. On an NVIDIA RTX A2000 GPU, the inference speed reached 520 frames per second. Our network enables real-time cataract diagnosis on low-cost GPUs, surpassing previous ResNet- and MobileNet-based benchmarks by ≥4% in F1-score and reducing output latency by 68%.
Artificial neural networks , Cataract , Diagnosis , Machine learning , Ophtalmology , You only look once
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Department of Machine Learning and Data Science, Institute of Automation and Information Technologies, Satbayev University, Almaty, Kazakhstan
Department of Cybersecurity, Institute of Automation and Information Technologies, Satbayev University, Almaty, Kazakhstan
Department of Machine Learning and Data Science
Department of Cybersecurity
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026