An Overview on Artificial Intelligence Techniques for Diagnosis of
Schizophrenia Based on Magnetic Resonance Imaging Modalities: Methods,
Challenges, and Future Works
- URL: http://arxiv.org/abs/2103.03081v1
- Date: Wed, 24 Feb 2021 11:12:06 GMT
- Title: An Overview on Artificial Intelligence Techniques for Diagnosis of
Schizophrenia Based on Magnetic Resonance Imaging Modalities: Methods,
Challenges, and Future Works
- Authors: Delaram Sadeghi, Afshin Shoeibi, Navid Ghassemi, Parisa Moridian, Ali
Khadem, Roohallah Alizadehsani, Mohammad Teshnehlab, J. Manuel Gorriz, Saeid
Nahavandi
- Abstract summary: 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.
The magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities.
- Score: 10.769064813142647
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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 the temporal and anterior lobes of hippocampus regions of brain get
affected by SZ. Also, increased volume of cerebrospinal fluid (CSF) and
decreased volume of white and gray matter can be observed due to this disease.
The 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 obtain
accurate diagnosis of SZ. This paper presents a comprehensive overview of
studies conducted on automated diagnosis of SZ using MRI modalities. Main
findings, various challenges, and future works in developing the automated SZ
detection are described in this paper.
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