Utilizing Deep Learning and SVM Models for Schizophrenia Detection and Symptom Severity Estimation Through Structural MRI

Abstract

The automated diagnosis of schizophrenia utilizing Magnetic Resonance Imaging (MRI) has been the subject of numerous investigations, the majority of which have primarily directed their focus towards disorder detection. This study, however, aims to transcend detection, endeavoring to estimate the severity of schizophrenia symptoms by leveraging structural MRI data. Such capabilities are anticipated to enhance the monitoring of treatment efficacy, guide clinical decision-making, and ultimately contribute to improved schizophrenia management. MRI datasets for schizophrenia patients (23) and control subjects (20) were sourced from the OpenNeuro database. Each structural MRI was processed to extract a grayscale image, which was then segmented into White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF). Statistical attributes-such as standard deviation, moment, and skewness-were derived from each segment to form feature representations of the grayscale images. An SVM with a linear kernel was trained, distinguishing schizophrenia subjects from healthy controls. Furthermore, for the schizophrenia subjects, the sums of their respective Scale for the Assessment of Positive Symptoms (SAPS) and Scale for the Assessment of Negative Symptoms (SANS) scores were computed. A twelve-layer artificial neural network (ANN) was then trained to estimate these symptom severity scores. The SVM model achieved optimal classification accuracy at 81.8%, while the ANN demonstrated a correlation coefficient of 0.811 and a mean absolute error of 1.44 on the validation dataset. This performance surpasses that of a comparable study estimating schizophrenia symptom severity from electroencephalogram (EEG) data, which yielded correlation coefficients ranging from -0.6 to -0.702. The paper concludes with a proposed software architecture for practical application of these findings.