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NUMERICAL METHODS AND DATA ANALYSIS
Application of artificial intelligence in ophthalmology for solving the problem of semantic segmentation of fundus images
N. S. Deminab, N. Yu. Ilyasovaab, R. A. Paringerab, D. V. Kirshab a Image Processing Systems Institute of the RAS - Branch of the FSRC "Crystallography and Photonics" RAS, Samara, Russia, Samara
b Samara National Research University
Abstract:
The paper presents main aspects of the application of artificial intelligence in ophthalmology for the diagnosis and treatment of eye diseases, considering the problem of semantic segmentation of fundus images as an example. The classic approach to semantic segmentation on the basis of textural features is compared to the proposed approach based on neural networks. Basic problems of using the neural network approach in biomedicine are formulated. We propose a new method for selecting an optimal zone of laser exposure for laser coagulation based on two neural networks. The first network is used for detecting anatomical objects in the fundus and the second one is used for selecting the area of macular edema. The region of interest is formed from the edema area while taking into account the location of anatomical objects in it. A comparative analysis of several architectures of neural networks for solving the problem of selecting the edema area is carried out. The best results in the selection of the edema area are shown by the neural network architecture of Unet++.
Keywords:
fundus image, laser coagulation, diabetic retinopathy, image processing, segmentation, neural network, artificial intelligence
Received: 27.01.2023 Accepted: 29.05.2023
Citation:
N. S. Demin, N. Yu. Ilyasova, R. A. Paringer, D. V. Kirsh, “Application of artificial intelligence in ophthalmology for solving the problem of semantic segmentation of fundus images”, Computer Optics, 47:5 (2023), 824–831
Linking options:
https://www.mathnet.ru/eng/co1184 https://www.mathnet.ru/eng/co/v47/i5/p824
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