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Journal of Integrative Neuroscience  2018, Vol. 17 Issue (4): 331-336    DOI: 10.31083/j.jin.2018.04.0410
Research article Previous articles | Next articles
Magnetic resonance imaging study of gray matter in schizophrenia based on XGBoost
Wang Yu1, *(), Zhang Na1, Yan Fengxia2, Gao Yanping1
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
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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.

Key words:  Schizophrenia      structural magnetic resonance imaging      feature extraction      classifier     
Submitted:  30 October 2017      Accepted:  18 December 2017      Published:  15 November 2018     
*Corresponding Author(s):  Wang Yu     E-mail:  wangyu@btbu.edu.cn

Cite this article: 

Wang Yu, Zhang Na, Yan Fengxia, Gao Yanping. Magnetic resonance imaging study of gray matter in schizophrenia based on XGBoost. Journal of Integrative Neuroscience, 2018, 17(4): 331-336.

URL: 

https://jin.imrpress.com/EN/10.31083/j.jin.2018.04.0410     OR     https://jin.imrpress.com/EN/Y2018/V17/I4/331

Fig. 1.  Examples of sliced gray matter images (from (a) to (h) respectively the 13th, 20th, 34th, 48th, 62nd, 69th, 76th, 83rd slices)

Table 1  Algorithm for the proposed method
1) Input the sMRI image;
2) Use formula (1) - (3) to preprocess the image, and to obtain the Img1, Img2, Img3 of each subject;
3) For the image obtained in step 2), use formula (4) - (11) to extract texture features T, T=(x11,x12,,x1n,y1), (x21,x22,,x2n,y2), , (xN1,xN2,,xNn,yN),xNiX?Rn,yi{0,+1};
4) Randomly divide the feature vectors into training and test sets, and put into KNN, SVM, LR, GB, XGBoost classifier for testing;
5) Tune parameters, and obtain the optimizing classification results.
Table 2  Description of the four classification results
Actual Group Predicted Group
Normal Abnormal
Normal TP FP
Abnormal FN TN
Fig.2.  Procedural flow chart of the evaluation method

Table 3  Characteristics of study participants
Variable Sample size Gender (male/female) Age (years) Mean/SD (range)
SCZ 100 62/38 35.50/37.08(13-60)
NC 100 49/51 34.73/34.86(17-65)
Value 0.064 a 0.53 b
Table 4  Classifier parameters
Classifiers Parameters
SVM C: {10-3,10-2,10-1,1,10,100,1000}; gamma: {0.001, 0.0001};
G B Tree min_samples_split: 9; min_samples_leaf: 1; max_depth: 9; max_features: 8;
Boosting learning_rate: 0.01; n_estimators: 90; subsample: 0.8;
XGBoost Booster col_sample_bytree: 0.8; min_child_weight: 1;
max_depth: 5; learning_rate: 0.01;
sub_sample: 0.9; gamma: 0;
General booster: gbtree;
Learning Task objective: binary-logistic; seed: 1.
Table 5  Experimental result comparison
Classifier ACC(%) AUC(%)
KNN 60 60.10
RBF-SVM 54 52.08
LR 52 52.24
GB 64 65.20
XGBoost 72 75.80
Fig.3.  ROC curve for (a) GB algorithm, and (b) XGBoost algorithm

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