# Machine learning techniques for the Schizophrenia diagnosis: A comprehensive review and future research directions

arXiv: https://arxiv.org/abs/2301.07496 · HTML: https://mlsanity.com/paper/2301.07496/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 91 references that are in the map.
- First posted on arXiv 16 Jan 2023.

## Builds on (most cited first, 8 of 8)

- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Brain MRI-based 3D Convolutional Neural Networks for Classification of Schizophrenia and Controls](https://mlsanity.com/paper/2003.08818/) (2020)
- [Multi-Kernel Capsule Network for Schizophrenia Identification](https://mlsanity.com/paper/1907.12827/) (2019)
- [Detecting Schizophrenia with 3D Structural Brain MRI Using Deep Learning](https://mlsanity.com/paper/2206.12980/) (2022)
- [Schizophrenia detection based on EEG using Recurrent Auto-Encoder framework](https://mlsanity.com/paper/2207.04262/) (2022)
- [Diagnosis of Schizophrenia: A comprehensive evaluation](https://mlsanity.com/paper/2203.11610/) (2022)
- [3D Deformable Convolutions for MRI classification](https://mlsanity.com/paper/1911.01898/) (2019)
- [Lightweight 3D Convolutional Neural Network for Schizophrenia diagnosis using MRI Images and Ensemble Bagging Classifier](https://mlsanity.com/paper/2211.02868/) (2022)

## Built on by (most cited first, 3 of 3)

- [An Explainable Deep Learning-Based Method For Schizophrenia Diagnosis Using Generative Data-Augmentation](https://mlsanity.com/paper/2310.16867/) (2023)
- [NeuroTree: Hierarchical Functional Brain Pathway Decoding for Mental Health Disorders](https://mlsanity.com/paper/2502.18786/) (2025)
- [Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023](https://mlsanity.com/paper/2309.12202/) (2023)

## Abstract (arXiv)

Schizophrenia (SCZ) is a brain disorder where different people experience different symptoms, such as hallucination, delusion, flat-talk, disorganized thinking, etc. In the long term, this can cause severe effects and diminish life expectancy by more than ten years. Therefore, early and accurate diagnosis of SCZ is prevalent, and modalities like structural magnetic resonance imaging (sMRI), functional MRI (fMRI), diffusion tensor imaging (DTI), and electroencephalogram (EEG) assist in witnessing the brain abnormalities of the patients. Moreover, for accurate diagnosis of SCZ, researchers have used machine learning (ML) algorithms for the past decade to distinguish the brain patterns of healthy and SCZ brains using MRI and fMRI images. This paper seeks to acquaint SCZ researchers with ML and to discuss its recent applications to the field of SCZ study. This paper comprehensively reviews state-of-the-art techniques such as ML classifiers, artificial neural network (ANN), deep learning (DL) models, methodological fundamentals, and applications with previous studies. The motivation of this paper is to benefit from finding the research gaps that may lead to the development of a new model for accurate SCZ diagnosis. The paper concludes with the research finding, followed by the future scope that directly contributes to new research directions.