A novel strategy for enhanced schizophrenia detection using established CNN architectures.

Schizophrenia, a complex psychotic condition, is challenging to diagnose due to its reliance on clinical assessments and behavioral evaluations. Neuroimaging studies, particularly structural MRI, have revealed reductions in grey matter volume in brain regions such as the temporal lobe and insula. This study combines deep learning and neuroimaging for precise automatic detection of schizophrenia. Using T1-weighted coronal MRI data from three publicly accessible datasets (MCICShare, COBRE, UCLA), a DeepLabv3+ model with ResNet50-based segmentation was employed to isolate the temporal lobe and insula, achieving segmentation accuracies of 96% and 97%, respectively. Enhanced visualization of isolated regions through color-to-grayscale conversion distinguished schizophrenia patients from controls, achieving an AUC of 0.99. Our study specifically focuses on the regions critically linked to both cognitive and emotional dysfunctions in schizophrenia. These results advance the literature by demonstrating improved diagnostic performance and reliability compared to traditional clinical assessments and earlier imaging-based methods.