Convolutional neural networks can diagnose schizophrenia

Abstract

Schizophrenia is a severe mental disorder that affects how individuals think, perceive, and behave, often making accurate and timely diagnosis a significant challenge for clinicians. Traditional diagnostic approaches, such as interviews and psychological tests, have limitations in capturing the complex neurological underpinnings of the condition. In recent years, machine learning and deep learning techniques have shown promise in improving diagnostic accuracy across a variety of medical domains. However, relatively few studies have applied these methods to schizophrenia diagnosis, despite their potential. In this study, we investigate whether convolutional neural networks can effectively diagnose schizophrenia using publicly available EEG data. We achieved classification accuracies of 98.26% in subject-independent settings and 91.21% in subject-dependent settings on the test data, using a fully connected layer based on a Multi-Layer Perceptron classifier. These results appear promising when compared to the current state of the art.