Background:
The importance of identifying the structural and functional abnormalities in the brain in early prediction and diagnosis of schizophrenia has grabbed the attention of both the neuroimaging scientist and clinician.
Objective:
The purpose of this study is to structure a review paper that find out the condition and recognize specific biomarkers of the schizophrenic brain.
Method:
Neuroimaging can be used to characterize brain structure, function, and chemistry by different non-invasive techniques such as computed tomography, magnetic resonance imaging, magnetic resonance spectroscopy, and positron emission tomography. The abnormalities in the brain can be used to discriminate the psychic disorder like schizophrenia from others. To find disease-related brain alterations in neuroimaging, structural neuroimaging studies provide the most consistent evidence in most of the studies.
Result:
In this work, a detailed survey has been made for finding the structural abnormalities in the brain from different neuroimaging techniques. Several image processing methods are used to acquire the brain images. Different Machine learning techniques, Optimization methods and Pattern recognition methods are used to predict the disease with specific biomarkers, and their results are emphasized. Thus, in this work, it is also highlighted the deep learning which shows a promising role in obtaining neuroimaging data to characterize disease-related alterations in brain structure.