Neural network application for semantic segmentation of fundus

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...

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Bibliographic Details
Published inKompʹûternaâ optika Vol. 46; no. 4; pp. 596 - 602
Main Authors Paringer, R.A., Mukhin, A.V., Ilyasova, N.Y., Demin, N.S.
Format Journal Article
LanguageEnglish
Published Samara National Research University 01.08.2022
Subjects
convolution
convolutional network
fundus
neural network
segmentation
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Summary: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.
ISSN:0134-2452
2412-6179
DOI:10.18287/2412-6179-CO-1010