Multimodal convolutional transformer (mct-dd): depression diagnosis through joint task analysis

Bibliographic Details
Parent link:	Молодежь и современные информационные технологии.— 2024.— С. 47-51
Main Author:	Firoz N.
Other Authors:	Berestneva O. G. Olga Grigorievna, Aksenov S. V. Sergey Vladimirovich
Summary:	A new deep learning method, Multimodal Convolutional Transformer, analyzes EEG and genetic data to diagnose MDD. This approach achieved high accuracy (97.16%) and surpasses other methods for early MDD detection, potentially aiding healthcare professionals Текстовый файл
Language:	English
Published:	2024
Series:	Искусственный интеллект, машинное обучение и большие данные
Subjects:	электронный ресурс труды учёных ТПУ genetics transformers EEG Deep Learning
Online Access:	http://earchive.tpu.ru/handle/11683/84880
Format:	Electronic Book Chapter
KOHA link:	https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=675176

Description
Summary:	A new deep learning method, Multimodal Convolutional Transformer, analyzes EEG and genetic data to diagnose MDD. This approach achieved high accuracy (97.16%) and surpasses other methods for early MDD detection, potentially aiding healthcare professionals Текстовый файл

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