Multimodal ensemble neural network system for skin cancer detection on heterogeneous dermatological data

Today, skin cancer is one of the leading causes of death in the world. Diagnosing skin cancer early is critical to increasing potential survival. Therefore, it is relevant to develop high-precision intelligent auxiliary diagnostic systems for detecting skin cancer in the early stages. Ensemble learn...

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Главные авторы:	Lyakhova, U. A., Ляхова, У. А., Lyakhov, P. A., Ляхов, П. А.
Формат:	Статья
Язык:	Russian
Опубликовано:	Saint Petersburg State University 2024
Темы:	Dermatological images Skin cancer Ensemble neural network Machine learning Heterogeneous data Melanoma Multimodal neural network Pigmented skin lesions
Online-ссылка:	https://dspace.ncfu.ru/handle/123456789/29404
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id	ir-123456789-29404
record_format	dspace
spelling	ir-123456789-294042024-12-13T11:51:46Z Multimodal ensemble neural network system for skin cancer detection on heterogeneous dermatological data Мультимодальная ансамблевая нейросетевая система обнаружения рака кожи на основе анализа гетерогенных дерматологических данных Lyakhova, U. A. Ляхова, У. А. Lyakhov, P. A. Ляхов, П. А. Dermatological images Skin cancer Ensemble neural network Machine learning Heterogeneous data Melanoma Multimodal neural network Pigmented skin lesions Today, skin cancer is one of the leading causes of death in the world. Diagnosing skin cancer early is critical to increasing potential survival. Therefore, it is relevant to develop high-precision intelligent auxiliary diagnostic systems for detecting skin cancer in the early stages. Ensemble learning is one of the current and promising methods for increasing the accuracy of intelligent classification systems by reducing the dispersion and variability of predictions of individual components of the overall system. The work proposes an ensemble intelligent system for analyzing heterogeneous dermatological data based on multimodal neural networks. The accuracy of the developed ensemble system was 85.92 %, which is 1.85 percentage points higher than the average accuracy of individual multimodal architectures for classifying heterogeneous dermatological data. The developed system can be used as a high-precision auxiliary diagnostic tool to help make a medical decision, which will increase the chance of early detection of pigmented oncological pathologies. 2024-12-13T11:50:41Z 2024-12-13T11:50:41Z 2024 Статья Lyakhova U.A., Lyakhov P.A. Multimodal ensemble neural network system for skin cancer detection on heterogeneous dermatological data // Vestnik Sankt-Peterburgskogo Universiteta, Prikladnaya Matematika, Informatika, Protsessy Upravleniya. - 2024. - 20 (2). - pp. 231 - 243. - DOI: 10.21638/spbu10.2024.208 https://dspace.ncfu.ru/handle/123456789/29404 ru Vestnik Sankt-Peterburgskogo Universiteta, Prikladnaya Matematika, Informatika, Protsessy Upravleniya application/pdf application/pdf Saint Petersburg State University
institution	СКФУ
collection	Репозиторий
language	Russian
topic	Dermatological images Skin cancer Ensemble neural network Machine learning Heterogeneous data Melanoma Multimodal neural network Pigmented skin lesions
spellingShingle	Dermatological images Skin cancer Ensemble neural network Machine learning Heterogeneous data Melanoma Multimodal neural network Pigmented skin lesions Lyakhova, U. A. Ляхова, У. А. Lyakhov, P. A. Ляхов, П. А. Multimodal ensemble neural network system for skin cancer detection on heterogeneous dermatological data
description	Today, skin cancer is one of the leading causes of death in the world. Diagnosing skin cancer early is critical to increasing potential survival. Therefore, it is relevant to develop high-precision intelligent auxiliary diagnostic systems for detecting skin cancer in the early stages. Ensemble learning is one of the current and promising methods for increasing the accuracy of intelligent classification systems by reducing the dispersion and variability of predictions of individual components of the overall system. The work proposes an ensemble intelligent system for analyzing heterogeneous dermatological data based on multimodal neural networks. The accuracy of the developed ensemble system was 85.92 %, which is 1.85 percentage points higher than the average accuracy of individual multimodal architectures for classifying heterogeneous dermatological data. The developed system can be used as a high-precision auxiliary diagnostic tool to help make a medical decision, which will increase the chance of early detection of pigmented oncological pathologies.
format	Статья
author	Lyakhova, U. A. Ляхова, У. А. Lyakhov, P. A. Ляхов, П. А.
author_facet	Lyakhova, U. A. Ляхова, У. А. Lyakhov, P. A. Ляхов, П. А.
author_sort	Lyakhova, U. A.
title	Multimodal ensemble neural network system for skin cancer detection on heterogeneous dermatological data
title_short	Multimodal ensemble neural network system for skin cancer detection on heterogeneous dermatological data
title_full	Multimodal ensemble neural network system for skin cancer detection on heterogeneous dermatological data
title_fullStr	Multimodal ensemble neural network system for skin cancer detection on heterogeneous dermatological data
title_full_unstemmed	Multimodal ensemble neural network system for skin cancer detection on heterogeneous dermatological data
title_sort	multimodal ensemble neural network system for skin cancer detection on heterogeneous dermatological data
publisher	Saint Petersburg State University
publishDate	2024
url	https://dspace.ncfu.ru/handle/123456789/29404
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Multimodal ensemble neural network system for skin cancer detection on heterogeneous dermatological data

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