# An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works

arXiv: https://arxiv.org/abs/2103.03081 · HTML: https://mlsanity.com/paper/2103.03081/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 345 references that are in the map.
- First posted on arXiv 24 Feb 2021.

## Builds on (most cited first, 29 of 29)

- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Concept Bottleneck Models](https://mlsanity.com/paper/2007.04612/) (2020)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [Out-of-Distribution Generalization via Risk Extrapolation (REx)](https://mlsanity.com/paper/2003.00688/) (2020)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search](https://mlsanity.com/paper/1812.03443/) (2018)
- [Feature Generating Networks for Zero-Shot Learning](https://mlsanity.com/paper/1712.00981/) (2017)
- [Generative Adversarial Network in Medical Imaging: A Review](https://mlsanity.com/paper/1809.07294/) (2018)
- [A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications](https://mlsanity.com/paper/2001.06937/) (2020)
- [3D Deep Learning on Medical Images: A Review](https://mlsanity.com/paper/2004.00218/) (2020)
- [Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets](https://mlsanity.com/paper/2010.14819/) (2020)
- [Deep Representation Learning with Part Loss for Person Re-Identification](https://mlsanity.com/paper/1707.00798/) (2017)
- [Relief-Based Feature Selection: Introduction and Review](https://mlsanity.com/paper/1711.08421/) (2017)
- [Epileptic Seizures Detection Using Deep Learning Techniques: A Review](https://mlsanity.com/paper/2007.01276/) (2020)
- [Recent Trends in Deep Learning Based Personality Detection](https://mlsanity.com/paper/1908.03628/) (2019)
- [Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review](https://mlsanity.com/paper/2007.01285/) (2020)
- [Blind Denoising Autoencoder](https://mlsanity.com/paper/1912.07358/) (2019)
- [Classification of Hand Movements from EEG using a Deep Attention-based LSTM Network](https://mlsanity.com/paper/1908.02252/) (2019)
- [Classification of EEG-Based Brain Connectivity Networks in Schizophrenia Using a Multi-Domain Connectome Convolutional Neural Network](https://mlsanity.com/paper/1903.08858/) (2019)
- [Brain MRI-based 3D Convolutional Neural Networks for Classification of Schizophrenia and Controls](https://mlsanity.com/paper/2003.08818/) (2020)
- [CapsNet comparative performance evaluation for image classification](https://mlsanity.com/paper/1805.11195/) (2018)
- [A Review on Neural Network Models of Schizophrenia and Autism Spectrum Disorder](https://mlsanity.com/paper/1906.10015/) (2019)
- [Multi-Kernel Capsule Network for Schizophrenia Identification](https://mlsanity.com/paper/1907.12827/) (2019)
- [A Closer Look at Disentangling in β-VAE](https://mlsanity.com/paper/1912.05127/) (2019)
- [Tensor-Based Fusion of EEG and FMRI to Understand Neurological Changes in Schizophrenia](https://mlsanity.com/paper/1612.02189/) (2016)
- [Material Recognition for Automated Progress Monitoring using Deep Learning Methods](https://mlsanity.com/paper/2006.16344/) (2020)
- [Deep Neural Generative Model of Functional MRI Images for Psychiatric Disorder Diagnosis](https://mlsanity.com/paper/1712.06260/) (2017)
- [Locally Linear Embedding and fMRI feature selection in psychiatric classification](https://mlsanity.com/paper/1908.06319/) (2019)

## Built on by (most cited first, 13 of 13)

- [Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models](https://mlsanity.com/paper/2109.01120/) (2021)
- [Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review](https://mlsanity.com/paper/2105.04881/) (2021)
- [Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review](https://mlsanity.com/paper/2206.11233/) (2022)
- [Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression](https://mlsanity.com/paper/2205.15858/) (2022)
- [An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works](https://mlsanity.com/paper/2105.14278/) (2021)
- [Advancements in Machine Learning and Deep Learning for Early Detection and Management of Mental Health Disorder](https://mlsanity.com/paper/2412.06147/) (2024)
- [Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence](https://mlsanity.com/paper/2210.14611/) (2022)
- [Spatial Sequence Attention Network for Schizophrenia Classification from Structural Brain MR Images](https://mlsanity.com/paper/2406.12683/) (2024)
- [Accurate Prediction Using Triangular Type-2 Fuzzy Linear Regression](https://mlsanity.com/paper/2109.05461/) (2021)
- [Connectional-Style-Guided Contextual Representation Learning for Brain Disease Diagnosis](https://mlsanity.com/paper/2306.05297/) (2023)
- [Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review](https://mlsanity.com/paper/2210.14909/) (2022)
- [Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023](https://mlsanity.com/paper/2309.12202/) (2023)
- [Diffusion with a Linguistic Compass: Steering the Generation of Clinically Plausible Future sMRI Representations for Early MCI Conversion Prediction](https://mlsanity.com/paper/2506.05428/) (2025)

## Abstract (arXiv)

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.