Texture analysis and classification of medical images based on deep learning methods

Karshakevich, D. V.

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Please use this identifier to cite or link to this item: https://elib.bsu.by/handle/123456789/280107

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DC Field	Value	Language
dc.contributor.author	Karshakevich, D. V.
dc.date.accessioned	2022-05-24T13:13:48Z	-
dc.date.available	2022-05-24T13:13:48Z	-
dc.date.issued	2022
dc.identifier.citation	Компьютерные технологии и анализ данных (CTDA’2022) : материалы III Междунар. науч.-практ. конф., Минск, 21–22 апр. 2022 г. / Белорус. гос. ун-т ; редкол.: В. В. Скакун (отв. ред.) [и др.]. – Минск : РИВШ, 2022. – С. 300-303.
dc.identifier.isbn	978-985-586-561-3
dc.identifier.uri	https://elib.bsu.by/handle/123456789/280107	-
dc.description	Секция «Биоинформатика»
dc.description.abstract	Different approaches to the classification of histological images of three different classes were experimentally investigated: metastasis-affected lymphoid tissue region, healthy lymphoid tissue region, and lymph node capsule. The paper investigated what quality of classification can be achieved by using only the classical approaches of texture feature extraction proposed by Haralick in 1970, then using neural networks and finally by combining both approaches
dc.language.iso	en
dc.publisher	Минск : РИВШ
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	ЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Кибернетика
dc.subject	ЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Биология
dc.title	Texture analysis and classification of medical images based on deep learning methods
dc.type	conference paper
Appears in Collections:	2022. Компьютерные технологии и анализ данных (CTDA’2022)

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