COMPARATIVE STUDY OF MODERN NEURAL NETWORK ARCHITECTURES FOR MEDICAL IMAGE SEGMENTATION PROBLEMS
https://doi.org/10.55452/1998-6688-2021-18-3-83-88
Abstract
Computer Vision is the area of Machine Learning that is responsible for machine perception of visual information. Image segmentation is a subfield of Computer Vision that solves the task of dividing a digital image into segments by their class label. One of the main problems in the subfield is the scarcity of data and the restoration of spatial information for the classified image. This article is a brief survey of current Biomedical Image Segmentation approaches, specifically Convolutional Neural Networks architectures and the morphological transformation for data augmentation.
About the Authors
A. Nagmetova
Kazakh-British technical university
Kazakhstan
Kazakhstan
Nagmetova Anar Aidarbekkyzy - MSc, Chief Business Analyst, Customer Base Management Sector
050000, Almaty
A. Aldosh
Kazakh-British technical university
Kazakhstan
Kazakhstan
Aldosh Adil Akylbaiuly - Master of Engineering Science, Data Developer, One Technologies LLP
050000, Almaty
References
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Review
For citations:
Nagmetova A., Aldosh A. COMPARATIVE STUDY OF MODERN NEURAL NETWORK ARCHITECTURES FOR MEDICAL IMAGE SEGMENTATION PROBLEMS. Herald of the Kazakh-British technical university. 2021;18(3):83-88. https://doi.org/10.55452/1998-6688-2021-18-3-83-88
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