Application of deep learning methods for automated analysis of retinal structures in ophthalmology
Kassymova A. Konyrkhanova A. Issembayeva A. Saimanova Z. Saltayev A. Ongarbayeva M. Issakova G.
April 2024Institute of Advanced Engineering and Science
International Journal of Electrical and Computer Engineering
2024#14Issue 21987 - 1995 pp.
This article examines a current area of research in the field of ophthalmology the use of deep learning methods for automated analysis of retinal structures. This work explores the use of deep learning methods such as EfficientNet and DenseNet for the automated analysis of retinal structures in ophthalmology. EfficientNet, originally proposed to balance between accuracy and computational efficiency, and DenseNet, based on dense connections between layers, are considered as tools for identifying and classifying retina features. Automated analysis includes identifying pathologies, assessing the degree of their development and, possibly, diagnosing various eye diseases. Experiments are performed on a dataset containing a variety of images of retinal structures. Results are evaluated using metrics of accuracy, sensitivity, and specificity. It is expected that the proposed deep learning methods can significantly improve the automated analysis of retinal images, which is important for the diagnosis and monitoring of eye diseases. As a result, the article highlights the significance and promise of using deep learning methods in ophthalmology for automated analysis of retinal structures. These methods help improve the early diagnosis, treatment and monitoring of eye diseases, which can ultimately lead to improved healthcare quality and improved patient lives.
Deep learning , DenseNet , EfficientNet , Eye diseases , Ophthalmology , Pathology
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Department of Information Technology, Zhangir Khan University, Uralsk, Kazakhstan
Department of Information Security, Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, Astana, Kazakhstan
Department of Information and Computing System, Abylkas Saginov Karaganda Technical University, Karaganda, Kazakhstan
Faculty of Natural Sciences, L.N. Gumilyov Eurasian National University, Astana, Kazakhstan
Department of Information and Communication Technologies, International Taraz Institute Named after Sherkhan Murtaza, Taraz, Kazakhstan
Department of Information Systems, Kazakh Agrotechnical Research University Named after S. Seifullin, Astana, Kazakhstan
Department of Information Technology
Department of Information Security
Department of Information and Computing System
Faculty of Natural Sciences
Department of Information and Communication Technologies
Department of Information Systems
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