  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.
