An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works
Article
Sadeghi, Delaram, Shoeibi, Afshin, Ghassemi, Navid, Moridian, Parisa, Khadem, Ali, Alizadehsani, Roohallah, Teshnehlab, Mohammad, Gorriz, Juan M., Khozeimeh, Fahime, Zhang, Yu-Dong, Nahavandi, Saeid and Acharya, U. Rajendra. 2022. "An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works." Computers in Biology and Medicine. 146. https://doi.org/10.1016/j.compbiomed.2022.105554
Article Title | An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works |
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ERA Journal ID | 5040 |
Article Category | Article |
Authors | Sadeghi, Delaram, Shoeibi, Afshin, Ghassemi, Navid, Moridian, Parisa, Khadem, Ali, Alizadehsani, Roohallah, Teshnehlab, Mohammad, Gorriz, Juan M., Khozeimeh, Fahime, Zhang, Yu-Dong, Nahavandi, Saeid and Acharya, U. Rajendra |
Journal Title | Computers in Biology and Medicine |
Journal Citation | 146 |
Article Number | 105554 |
Number of Pages | 34 |
Year | 2022 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0010-4825 |
1879-0534 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.compbiomed.2022.105554 |
Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0010482522003468 |
Abstract | Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed that SZ affects the temporal and anterior lobes of hippocampus regions of the brain. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. Magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder, owing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed with advanced image/signal processing methods to accurately diagnose SZ. This paper presents a comprehensive overview of studies conducted on the automated diagnosis of SZ using MRI modalities. First, an AI-based computer aided-diagnosis system (CADS) for SZ diagnosis and its relevant sections are presented. Then, this section introduces the most important conventional machine learning (ML) and deep learning (DL) techniques in the diagnosis of diagnosing SZ. A comprehensive comparison is also made between ML and DL studies in the discussion section. In the following, the most important challenges in diagnosing SZ are addressed. Future works in diagnosing SZ using AI techniques and MRI modalities are recommended in another section. Results, conclusion, and research findings are also presented at the end. |
Keywords | Conventional machine learning; Schizophrenia; Diagnosis; MRI; Deep learning ; Neuroscience |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | Islamic Azad University, Iran |
K. N. Toosi University of Technology, Iran | |
Deakin University | |
University of Granada, Spain | |
University of Cambridge, United Kingdom | |
University of Leicester, United Kingdom | |
Harvard University, United States | |
Ngee Ann Polytechnic, Singapore | |
Asia University, Taiwan | |
Singapore University of Social Sciences (SUSS), Singapore |
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