Depression recognition method based on deep convolutional neural networks

Junzhe Li; Zijian Li

doi:10.1117/12.3041827

11 September 2024 Depression recognition method based on deep convolutional neural networks

Junzhe Li, Zijian Li

Proceedings Volume 13253, Fourth International Conference on Signal Image Processing and Communication (ICSIPC 2024); 1325313 (2024) https://doi.org/10.1117/12.3041827
Event: 4th International Conference on Signal Image Processing and Communication, 2024, Xi'an, China

Abstract

Depression, as a severe mental disorder, is characterized primarily by significant and persistent mood disturbance and anhedonia. Currently, its diagnosis relies mainly on clinician interviews, leading to a high rate of misdiagnosis. Deep learning has shown promising research outcomes in areas such as image processing and speech recognition, addressing the high misdiagnosis rate of depression in clinical practice. This study collected resting-state and reward/punishment task-related brain functional information from 22 depression patients and 15 healthy controls. By integrating deep learning methods with brain functional information, depression identification was conducted. To address issues related to depression identification features based on task-related brain function and its task stimuli, this study utilized locally consistent brain functional imaging data from depression patients and healthy controls during reward/punishment tasks as features for depression identification. Experimental results were compared among reward tasks, punishment tasks, and both tasks combined. The results showed that classification results under reward task stimuli were superior, achieving recognition accuracies, sensitivities, specificities, precisions, and F1 scores of 88.08%, 88.50%, 87.83%, 88.18%, and 0.88, respectively.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Junzhe Li and Zijian Li "Depression recognition method based on deep convolutional neural networks", Proc. SPIE 13253, Fourth International Conference on Signal Image Processing and Communication (ICSIPC 2024), 1325313 (11 September 2024); https://doi.org/10.1117/12.3041827

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