期刊
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
卷 19, 期 3, 页码 1459-1471出版社
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2021.3053181
关键词
Three-dimensional displays; Feature extraction; Deep learning; Medical diagnostic imaging; Magnetic resonance imaging; Convolution; Kernel; Neuropsychiatric disorder; phenotypic record; image features; hybrid features; classification
类别
资金
- National Natural Science Foundation of China [61802442, 61877059]
- 111 Project [B18059]
- Hunan Sci-tech Innovation Programme [2020GK2019]
- Hunan Provincial Science and Technology Program [2018WK4001]
- Natural Science Foundation of Hunan Province [2018JJ2084]
This paper proposes an ensemble hybrid features selection method for the classification of neuropsychiatric disorders, which improves the performance of classification methods. The importance of phenotypic features and image features in different classification tasks is analyzed.
Magnetic resonance imagings (MRIs) are providing increased access to neuropsychiatric disorders that can be made available for advanced data analysis. However, the single type of data limits the ability of psychiatrists to distinguish the subclasses of this disease. In this paper, we propose an ensemble hybrid features selection method for the neuropsychiatric disorder classification. The method consists of a 3D DenseNet and a XGBoost, which are used to select the image features from structural MRI images and the phenotypic feature from phenotypic records, respectively. The hybrid feature is composed of image features and phenotypic features. The proposed method is validated in the Consortium for Neuropsychiatric Phenomics (CNP) dataset, where samples are classified into one of the four classes (healthy controls (HC), attention deficit hyperactivity disorder (ADHD), bipolar disorder (BD), and schizophrenia (SD)). Experimental results show that the hybrid feature can improve the performance of classification methods. The best accuracy of binary and multi-class classification can reach 91.22 and 78.62 percent, respectively. We analyze the importance of phenotypic features and image features in different classification tasks. The importance of the structure MRI images is highlighted by incorporating phenotypic features with image features to generate hybrid features. We also visualize the features of three neuropsychiatric disorders and analyze their locations in the brain region.
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