Diagnosis of schizophrenia using brain resting-state fMRI with activity maps based on deep learning

In recent years, the use of medical imaging in the analysis of human body structure and diagnosis of diseases has greatly increased. Schizophrenia is a serious psychiatric illness which needs early and accurate diagnosis. Functional magnetic resonance imaging (fMRI) with appropriate spatial resolution is a powerful technique for visualizing human brain activity. One of the major challenges in classifying these images is the high-dimensional fMRI images along with the poor quality of these data. In this study, a general framework for classifying images into two groups of healthy and schizophrenic patients is presented. In this method, after preprocessing fMRI images, functional connectivity analysis is used to extract the features. After extracting functional mappings, we use three-dimensional convolutional neural network and long short-term memory recurrent network to extract spatial and temporal information to classify activity maps. Our results show that the use of these features can lead to a strong classification on the COBRE dataset with an accuracy of 92.32%, which is very promising.