An ensemble approach using multidimensional convolutional neural networks in wavelet domain for schizophrenia classification from sMRI data.

Schizophrenia is a complicated mental condition marked by disruptions in thought processes, perceptions, and emotional responses, which can cause severe impairment in everyday functioning. sMRI is a non-invasive neuroimaging technology that visualizes the brain's structure while providing precise information on its anatomy and potential problems. This paper investigates the role of multidimensional Convolutional Neural Network (CNN) architectures: 1D-CNN, 2D-CNN and 3D-CNN, using the DWT subbands of sMRI data. 1D-CNN involves energy features extracted from the CD subband of sMRI data. The sum of gradient magnitudes of CD subband, known as energy feature, highlights diagonal high frequency elements associated with schizophrenia. 2D-CNN uses the CH subband decomposed by DWT that enables feature extraction from horizontal high frequency coefficients of sMRI data. In the case of 3D-CNNs, the CV subband is used which leads to volumetric feature extraction from vertical high frequency coefficients. Feature extraction in DWT domain explores textural changes, edges, coarse and fine details present in sMRI data from which the multidimensional feature extraction is carried out for classification.Through maximum voting technique, the proposed model optimizes schizophrenia classification from the multidimensional CNN models. The generalization of the proposed model for the two datasets proves convincing in improving the classification accuracy. The multidimensional CNN architectures achieve an average accuracy of 93.2%, 95.8%, and 98.0%, respectively, while the proposed model achieves an average accuracy of 98.9%.