Revolutionizing Schizophrenia Diagnosis: A Transfer Learning Approach to Accurate Classification

Abstract

Schizophrenia is one of the most serious mental disorders that has been both widely researched and extensively feared. It is among those mental illnesses which are relatively less unveiled to the world but its effects upon people living with it are undoubtedly profound. Significance of identification of schizophrenia is of vital importance because it has become a critical and challenging problem. In the past decade, different artificial intelligence techniques have been introduced to assist mental health providers, but no satisfactory results have been obtained for identification of schizophrenia and there is no front-end application to which doctors are interacted and to use these techniques or algorithms knowledge of programming required. Furthermore, Neuroimaging technique like functional magnetic resonance imaging (FMRI) is not able to perform adequate temporal sampling due to slow bold response. In this research work, pre-defined fMRI regions specifically related to the schizophrenia are used for mapping. Schizophrenia disease has been classified through transfer learning algorithm Alex Net. In order to evaluate algorithm results, we used the standard measures accuracy, sensitivity, specificity, prevalence, likelihood ratio positive, and likelihood ratio negative. This project provides front end window form designed using Tkinter python module where doctors upload user’s details and FMRI 4D image and window form first convert into 2d images and then take specific slice to classify is image is positive or negative and store result in database.