Diagnosis of Depressive Disorder Model on Facial Expression Based on Fast R-CNN

Young-Shin Lee; Won-Hyung Park

doi:10.3390/diagnostics12020317

Diagnosis of Depressive Disorder Model on Facial Expression Based on Fast R-CNN

Diagnostics (Basel). 2022 Jan 27;12(2):317. doi: 10.3390/diagnostics12020317.

Authors

Young-Shin Lee¹, Won-Hyung Park²

Affiliations

¹ Department of Nursing, Fareast University, Eumseong 27601, Korea.
² Department of Information Security Protection Engineering, Sangmyung University, Cheonan 31066, Korea.

Abstract

This study examines related literature to propose a model based on artificial intelligence (AI), that can assist in the diagnosis of depressive disorder. Depressive disorder can be diagnosed through a self-report questionnaire, but it is necessary to check the mood and confirm the consistency of subjective and objective descriptions. Smartphone-based assistance in diagnosing depressive disorders can quickly lead to their identification and provide data for intervention provision. Through fast region-based convolutional neural networks (R-CNN), a deep learning method that recognizes vector-based information, a model to assist in the diagnosis of depressive disorder can be devised by checking the position change of the eyes and lips, and guessing emotions based on accumulated photos of the participants who will repeatedly participate in the diagnosis of depressive disorder.

Keywords: deep learning; depressive disorder; diagnosis; facial expression; fast R-CNN.