ViT-Based Face Diagnosis Images Analysis for Schizophrenia Detection
Objectives: Computer-aided schizophrenia (SZ) detection methods mainly depend on electroencephalogram and brain magnetic resonance images, which both capture physical signals from patients’ brains. These inspection techniques take too much time and affect patients’ compliance and cooperation, while...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Brain Sciences |
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| Online Access: | https://www.mdpi.com/2076-3425/15/1/30 |
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| author | Huilin Liu Runmin Cao Songze Li Yifan Wang Xiaohan Zhang Hua Xu Xirong Sun Lijuan Wang Peng Qian Zhumei Sun Kai Gao Fufeng Li |
| author_facet | Huilin Liu Runmin Cao Songze Li Yifan Wang Xiaohan Zhang Hua Xu Xirong Sun Lijuan Wang Peng Qian Zhumei Sun Kai Gao Fufeng Li |
| author_sort | Huilin Liu |
| collection | DOAJ |
| description | Objectives: Computer-aided schizophrenia (SZ) detection methods mainly depend on electroencephalogram and brain magnetic resonance images, which both capture physical signals from patients’ brains. These inspection techniques take too much time and affect patients’ compliance and cooperation, while difficult for clinicians to comprehend the principle of detection decisions. This study proposes a novel method using face diagnosis images based on traditional Chinese medicine principles, providing a non-invasive, efficient, and interpretable alternative for SZ detection. Methods: An innovative face diagnosis image analysis method for SZ detection, which learns feature representations based on Vision Transformer (ViT) directly from face diagnosis images. It provides a face features distribution visualization and quantitative importance of each facial region and is proposed to supplement interpretation and to increase efficiency in SZ detection while keeping a high detection accuracy. Results: A benchmarking platform comprising 921 face diagnostic images, 6 benchmark methods, and 4 evaluation metrics was established. The experimental results demonstrate that our method significantly improves SZ detection performance with a 3–10% increase in accuracy scores. Additionally, it is found that facial regions rank in descending order according to importance in SZ detection as eyes, mouth, forehead, cheeks, and nose, which is exactly consistent with the clinical traditional Chinese medicine experience. Conclusions: Our method fully leverages semantic feature representations of first-introduced face diagnosis images in SZ, offering strong interpretability and visualization capabilities. It not only opens a new path for SZ detection but also brings new tools and concepts to the research and application in the field of mental illness. |
| format | Article |
| id | doaj-art-109c91abebc94275bd3505a014729135 |
| institution | Kabale University |
| issn | 2076-3425 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj-art-109c91abebc94275bd3505a0147291352025-01-24T13:25:44ZengMDPI AGBrain Sciences2076-34252024-12-011513010.3390/brainsci15010030ViT-Based Face Diagnosis Images Analysis for Schizophrenia DetectionHuilin Liu0Runmin Cao1Songze Li2Yifan Wang3Xiaohan Zhang4Hua Xu5Xirong Sun6Lijuan Wang7Peng Qian8Zhumei Sun9Kai Gao10Fufeng Li11School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, ChinaState Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, ChinaShanghai Pudong New Area Mental Health Center, Tongji University, Shanghai 200124, ChinaSchool of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, ChinaSchool of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, ChinaSchool of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, ChinaSchool of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, ChinaSchool of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, ChinaObjectives: Computer-aided schizophrenia (SZ) detection methods mainly depend on electroencephalogram and brain magnetic resonance images, which both capture physical signals from patients’ brains. These inspection techniques take too much time and affect patients’ compliance and cooperation, while difficult for clinicians to comprehend the principle of detection decisions. This study proposes a novel method using face diagnosis images based on traditional Chinese medicine principles, providing a non-invasive, efficient, and interpretable alternative for SZ detection. Methods: An innovative face diagnosis image analysis method for SZ detection, which learns feature representations based on Vision Transformer (ViT) directly from face diagnosis images. It provides a face features distribution visualization and quantitative importance of each facial region and is proposed to supplement interpretation and to increase efficiency in SZ detection while keeping a high detection accuracy. Results: A benchmarking platform comprising 921 face diagnostic images, 6 benchmark methods, and 4 evaluation metrics was established. The experimental results demonstrate that our method significantly improves SZ detection performance with a 3–10% increase in accuracy scores. Additionally, it is found that facial regions rank in descending order according to importance in SZ detection as eyes, mouth, forehead, cheeks, and nose, which is exactly consistent with the clinical traditional Chinese medicine experience. Conclusions: Our method fully leverages semantic feature representations of first-introduced face diagnosis images in SZ, offering strong interpretability and visualization capabilities. It not only opens a new path for SZ detection but also brings new tools and concepts to the research and application in the field of mental illness.https://www.mdpi.com/2076-3425/15/1/30schizophrenia detectionface diagnosis imagesVision Transformer (ViT)clinical facial features analysis |
| spellingShingle | Huilin Liu Runmin Cao Songze Li Yifan Wang Xiaohan Zhang Hua Xu Xirong Sun Lijuan Wang Peng Qian Zhumei Sun Kai Gao Fufeng Li ViT-Based Face Diagnosis Images Analysis for Schizophrenia Detection Brain Sciences schizophrenia detection face diagnosis images Vision Transformer (ViT) clinical facial features analysis |
| title | ViT-Based Face Diagnosis Images Analysis for Schizophrenia Detection |
| title_full | ViT-Based Face Diagnosis Images Analysis for Schizophrenia Detection |
| title_fullStr | ViT-Based Face Diagnosis Images Analysis for Schizophrenia Detection |
| title_full_unstemmed | ViT-Based Face Diagnosis Images Analysis for Schizophrenia Detection |
| title_short | ViT-Based Face Diagnosis Images Analysis for Schizophrenia Detection |
| title_sort | vit based face diagnosis images analysis for schizophrenia detection |
| topic | schizophrenia detection face diagnosis images Vision Transformer (ViT) clinical facial features analysis |
| url | https://www.mdpi.com/2076-3425/15/1/30 |
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