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http://earchive.tpu.ru/handle/11683/84880| Title: | Multimodal convolutional transformer (mct-dd): depression diagnosis through joint task analysis |
| Authors: | Firoz, N. Berestneva, Olga Grigorievna Aksenov, Sergey Vladimirovich |
| Keywords: | genetics; transformers; EEG; Deep Learning |
| Issue Date: | 2024 |
| Publisher: | Томский политехнический университет |
| Citation: | Firoz, N. Multimodal convolutional transformer (mct-dd): depression diagnosis through joint task analysis / Firoz N., Beresteneva O. G., Aksyonov S. V. // Молодежь и современные информационные технологии : сборник трудов XXI Международной научно-практической конференции студентов, аспирантов и молодых ученых, 15-18 апреля 2024 г., Томск. — Томск : Изд-во ТПУ, 2024. — С. 47-51. |
| Abstract: | 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 |
| URI: | http://earchive.tpu.ru/handle/11683/84880 |
| Appears in Collections: | Материалы конференций |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| conference_tpu-2024-C04_p47-51.pdf | 309,43 kB | Adobe PDF | View/Open |
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