Diagnosis of depression based on facial multimodal data
Bibliographic Data
| ID | 15526255 |
|---|---|
| Authors | Na Jin (0000-0001-9443-2784, Shanghai University), Nani Jin, Rongzhen Ye (0009-0005-9547-072X, Shanghai University), Renjia Ye, Peng Li (0000-0002-7138-430X, Central South University, corresponding author) |
| Year | 2025 |
| Volume | 16 |
| Pages | 1508772-1508772 |
| Publication date | 2025-01-28 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Psychiatry (JOURNAL) |
| Journal identifiers | ISSN: 1664-0640 • E-ISSN: 1664-0640 |
| Publisher | Frontiers Media (PUBLISHER • CH) |
| DOI | 10.3389/fpsyt.2025.1508772 |
| PMID | 39935533 |
| OpenAlex | W4406919848 |
| Language | EN |
| Citations received | 1 |
| References cited | 16 |
Compared with existing methods, our model shows excellent performance in multi-modal information fusion, which is suitable for early evaluation of depression
Convolutional neural network · Deep learning · Depression (economics · Distress · Feature (linguistics · Feature extraction · Machine learning · Modal · Clinical Psychology · Computer Science · Emotion and Mood Recognition · Machine Learning in Healthcare · Mental Health via Writing · Psychology · Artificial Intelligence
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |