  Structural alterations have been thoroughly investigated in the brain during
the early onset of schizophrenia (SCZ) with the development of neuroimaging
methods. The objective of the paper is an efficient classification of SCZ in 2
different classes: Cognitive Normal (CN), and SCZ using magnetic resonance
imaging (MRI) images. This paper proposed a lightweight 3D convolutional neural
network (CNN) based framework for SCZ diagnosis using MRI images. In the
proposed model, lightweight 3D CNN is used to extract both spatial and spectral
features simultaneously from 3D volume MRI scans, and classification is done
using an ensemble bagging classifier. Ensemble bagging classifier contributes
to preventing overfitting, reduces variance, and improves the model's accuracy.
The proposed algorithm is tested on datasets taken from three benchmark
databases available as open-source: MCICShare, COBRE, and fBRINPhase-II. These
datasets have undergone preprocessing steps to register all the MRI images to
the standard template and reduce the artifacts. The model achieves the highest
accuracy 92.22%, sensitivity 94.44%, specificity 90%, precision 90.43%, recall
94.44%, F1-score 92.39% and G-mean 92.19% as compared to the current
state-of-the-art techniques. The performance metrics evidenced the use of this
model to assist the clinicians for automatic accurate diagnosis of SCZ.
