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Structural alterations have been thoroughly investigated in the brain duringthe early onset of schizophrenia (SCZ) with the development of neuroimagingmethods. The objective of the paper is an efficient classification of SCZ in 2different classes: Cognitive Normal (CN), and SCZ using magnetic resonanceimaging (MRI) images. This paper proposed a lightweight 3D convolutional neuralnetwork (CNN) based framework for SCZ diagnosis using MRI images. In theproposed model, lightweight 3D CNN is used to extract both spatial and spectralfeatures simultaneously from 3D volume MRI scans, and classification is doneusing an ensemble bagging classifier. Ensemble bagging classifier contributesto preventing overfitting, reduces variance, and improves the model's accuracy.The proposed algorithm is tested on datasets taken from three benchmarkdatabases available as open-source: MCICShare, COBRE, and fBRINPhase-II. Thesedatasets have undergone preprocessing steps to register all the MRI images tothe standard template and reduce the artifacts. The model achieves the highestaccuracy 92.22%, sensitivity 94.44%, specificity 90%, precision 90.43%, recall94.44%, F1-score 92.39% and G-mean 92.19% as compared to the currentstate-of-the-art techniques. The performance metrics evidenced the use of thismodel to assist the clinicians for automatic accurate diagnosis of SCZ.

Lightweight 3D Convolutional Neural Network for Schizophrenia diagnosis using MRI Images and Ensemble Bagging Classifier