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Neural network application for semantic segmentation of fundus
R.A. Paringer 1,2, A.V. Mukhin 1, N.Y. Ilyasova 1,2, N.S. Demin 1,2

Samara National Research University, 443086, Samara, Russia, Moskovskoye Shosse 34;
IPSI RAS – Branch of the FSRC "Crystallography and Photonics" RAS,
443001, Samara, Russia, Molodogvardeyskaya 151

 PDF, 1294 kB

DOI: 10.18287/2412-6179-CO-1010

Pages: 596-602.

Full text of article: Russian language.

Advances in the neural networks have brought revolution in many areas, especially those related to image processing and analysis. The most complex is a task of analyzing biomedical data due to a limited number of samples, imbalanced classes, and low-quality labelling. In this paper, we look into the possibility of using neural networks when solving a task of semantic segmentation of fundus. The applicability of the neural networks is evaluated through a comparison of image segmentation results with those obtained using textural features. The neural networks are found to be more accurate than the textural features both in terms of precision (~25%) and recall (~50%). Neural networks can be applied in biomedical image segmentation in combination with data balancing algorithms and data augmentation techniques.

convolution, neural network, convolutional network, segmentation, fundus.

Paringer RA, Mukhin AV, Ilyasova NY, Demin NS. Neural network application for semantic segmentation of fundus. Computer Optics 2022; 46(4): 596-602. DOI: 10.18287/2412-6179-CO-1010.

This work was funded by the Russian Foundation for Basic Research under RFBR grant No. 19-29-01135 and the Ministry of Science and Higher Education of the Russian Federation within a government project of Samara University and FSRC "Crystallography and Photonics" RAS.


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