Spatial and frequency domain-based feature fusion for accurate detection of schizophrenia using AI-driven approaches.

Schizophrenia is a neuropsychiatric disorder that hampers brain functions and causes hallucinations, delusions, and bizarre behavior. The stigmatization associated with this disabling disorder drives the need to build diagnostic models with impeccable performances. Neuroimaging modality such as structural MRI is coupled with machine learning techniques to perform schizophrenia diagnosis with increased reliability. We investigate the structural aberrations present in the structural MR images using machine learning techniques. In this study, we propose a new hybrid approach using spatial and frequency domain-based features for the early automated detection of schizophrenia using machine learning techniques. The spatial or texture features are extracted using the local binary pattern method, and frequency-based features, including magnitude and phase, are extracted using the fast fourier transform feature extraction technique. Hybrid features, combining spatial and frequency-based features, are utilized for schizophrenia classification using support vector machine, random forest, and k-nearest neighbor with stratified 10-fold cross-validation. The support vector machine and random forest classifiers achieve encouraging detection performances on the hybrid feature set, with 86.5% and 85.1% accuracy, respectively. Among the three classifiers, k-nearest neighbor shows outstanding detection performance with an accuracy of 98.1%. The precision and recall achieved by the k-nearest neighbor classifier are 98.1% and 98.0% respectively, reflecting accurate detection of schizophrenia by the proposed model.