Deep Learning Techniques for Depression Assessment
| Affiliation auteurs | !!!! Error affiliation !!!! |
| Titre | Deep Learning Techniques for Depression Assessment |
| Type de publication | Conference Paper |
| Year of Publication | 2018 |
| Auteurs | S'adan MAhmad Hazi, Pampouchidou A, Meriaudeau F |
| Conference Name | 2018 INTERNATIONAL CONFERENCE ON INTELLIGENT AND ADVANCED SYSTEM (ICIAS 2018) / WORLD ENGINEERING, SCIENCE & TECHNOLOGY CONGRESS (ESTCON) |
| Publisher | Univ Teknologi Petronas, Elect & Elect Engn Dept |
| Conference Location | 345 E 47TH ST, NEW YORK, NY 10017 USA |
| ISBN Number | 978-1-5386-7269-3 |
| Mots-clés | AVEC dataset, Convolutional Neural Network (CNN), Depression assessment |
| Résumé | Depression is a typical mood disorder, which affects a significant number of individuals worldwide at an increasing rate. Objective measures for early detection of signs related to depression could be beneficial for clinicians with regards to a decision support system. In this paper, assessment of depression is done by applying three deep learning techniques of Convolutional Neural Network (CNN). These techniques are transfer learning using AlexNet, fine-tuning using AlexNet and building an end to end CNN. The inputs of the CNNs are a combination of Motion History Image, Landmark Motion History Image and Gabor Motion History Image, and have been generated on a depression dataset. Accuracy of the three deep learning techniques are computed. As of now, transfer learning technique achieved a result comparable to the state of the art, of 83% accuracy. |