IMR Press / JIN / Volume 17 / Issue 4 / DOI: 10.31083/j.jin.2018.04.0410
Open Access Research article
Magnetic resonance imaging study of gray matter in schizophrenia based on XGBoost
Show Less
1 Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University,Beijing, 100048, China
2 School of Science, National University of Defense Technology, Changsha, 410073, China
J. Integr. Neurosci. 2018 , 17(4), 331–336; https://doi.org/10.31083/j.jin.2018.04.0410
Submitted: 30 October 2017 | Accepted: 18 December 2017 | Published: 15 November 2018
Abstract

Brain structural abnormalities of schizophrenia subjects are often considered as the main neurobiological basis of this brain disease. Therefore, with the rapid development of artificial intelligence and medical imaging technologies, machine learning and structural magnetic resonance imaging have often been applied to computer-aided diagnosis of brain diseases such as schizophrenia, Alzheimer, glioma segmentation, etc. In this paper, statistical analysis of schizophrenic and normal subjects is initially made. Additionally, a slicing and weighted average method is proposed for gray matter images of the structural magnetic resonance imaging stored as three-dimensional volume data. Grey-level co-occurrence matrix texture features from the previously processed gray matter images of structural magnetic resonance imaging are then extracted and normalized. Finally, an eXtreme Gradient Boosting classifier is used for schizophrenia classification. Experiments employed 100 schizophrenic subjects and 100 normal controls. Results show the proposed method improves the respective classification accuracy of healthy controls and schizophrenic subjects by 8% and 10.6% of the area under the receiver operating characteristic. This suggests that the textural features of gray matter changes may be of diagnostic value in schizophrenia.

Keywords
Schizophrenia
structural magnetic resonance imaging
feature extraction
classifier
Figures
Fig. 1.
Share
Back to top