Survey on Structural Neuro Imaging for the Identification of Brain
Abnormalities in Schizophrenia

N.      Swathi; S.      Prabha
Abstract

Background: The importance of identifying the structural and functional abnormalities in the brain in the early prediction and diagnosis of schizophrenia has attracted the attention of neuroimaging scientists and clinicians.
Objective: The purpose of this study is to structure a review paper that recognizes specific biomarkers of the schizophrenic brain.
Methods: 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 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.
The review discusses the major issues and findings in structural neuroimaging studies of schizophrenia. In particular, the data is collected from different papers that concentrated on the brain affected regions of different subjects and made a conclusion out of it.
Results: In this work, a detailed survey has been done to find structural abnormalities in the brain from different neuroimaging techniques. Several image processing methods are used to acquire 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, deep learning is also highlighted, which shows a promising role in obtaining neuroimaging data to characterize disease-related alterations in brain structure.
Keywords: Machine learning, schizophrenia, neuro imaging, deep learning, pattern recognition, brain abnormalities.
Graphical Abstract

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Cite As
Current Medical Imaging

Survey on Structural Neuro Imaging for the Identification of Brain Abnormalities in Schizophrenia

Abstract

Graphical Abstract
