Auxiliary diagnosis of depression based on bimodal magnetic resonance imaging and decision level fusion(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2022年第3期
- Page:
- 378-383
- Research Field:
- 医学人工智能
- Publishing date:
Info
- Title:
- Auxiliary diagnosis of depression based on bimodal magnetic resonance imaging and decision level fusion
- Author(s):
- DUAN Yifan; WANG Yu; FU Changyang; XIAO Hongbing; XING Suxia
- School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
- Keywords:
- Keywords: depression structural magnetic resonance imaging functional magnetic resonance imaging data fusion
- PACS:
- R318；R749.41
- DOI:
- DOI:10.3969/j.issn.1005-202X.2022.03.019
- Abstract:
- Abstract: A depression classification algorithm based on structural and functional magnetic resonance imaging data fusion is proposed. After extracting functional and structural data features by functional brain network and deep learning network, and obtaining class probability, soft voting method and weighted voting method are used to fuse two kinds of probability data at decision level for fully extracting the data information of functional and structural magnetic resonance imaging, thereby obtaining more accurate classification results. The test results show that the data fusion method can effectively improve the performance in depression classification, achieving an accuracy of 91.34% and a recall rate of 96.62%, which testifies that the proposed method can better realize the auxiliary diagnosis and prognosis of depression.
Last Update: 2022-03-28