# Lightweight 3D Convolutional Neural Network for Schizophrenia diagnosis using MRI Images and Ensemble Bagging Classifier

arXiv: https://arxiv.org/abs/2211.02868 · HTML: https://mlsanity.com/paper/2211.02868/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 5 Nov 2022.

## Builds on (most cited first, 6 of 6)

- [Artificial neural networks for neuroscientists: A primer](https://mlsanity.com/paper/2006.01001/) (2020)
- [Random Vector Functional Link Neural Network based Ensemble Deep Learning](https://mlsanity.com/paper/1907.00350/) (2019)
- [Brain MRI-based 3D Convolutional Neural Networks for Classification of Schizophrenia and Controls](https://mlsanity.com/paper/2003.08818/) (2020)
- [Detecting Schizophrenia with 3D Structural Brain MRI Using Deep Learning](https://mlsanity.com/paper/2206.12980/) (2022)
- [Diagnosis of Schizophrenia: A comprehensive evaluation](https://mlsanity.com/paper/2203.11610/) (2022)
- [RobIn: A Robust Interpretable Deep Network for Schizophrenia Diagnosis](https://mlsanity.com/paper/2203.17085/) (2022)

## Built on by (most cited first, 1 of 1)

- [Machine learning techniques for the Schizophrenia diagnosis: A comprehensive review and future research directions](https://mlsanity.com/paper/2301.07496/) (2023)

## Abstract (arXiv)

Structural alterations have been thoroughly investigated in the brain during the early onset of schizophrenia (SCZ) with the development of neuroimaging methods. The objective of the paper is an efficient classification of SCZ in 2 different classes: Cognitive Normal (CN), and SCZ using magnetic resonance imaging (MRI) images. This paper proposed a lightweight 3D convolutional neural network (CNN) based framework for SCZ diagnosis using MRI images. In the proposed model, lightweight 3D CNN is used to extract both spatial and spectral features simultaneously from 3D volume MRI scans, and classification is done using an ensemble bagging classifier. Ensemble bagging classifier contributes to preventing overfitting, reduces variance, and improves the model's accuracy. The proposed algorithm is tested on datasets taken from three benchmark databases available as open-source: MCICShare, COBRE, and fBRINPhase-II. These datasets have undergone preprocessing steps to register all the MRI images to the standard template and reduce the artifacts. The model achieves the highest accuracy 92.22%, sensitivity 94.44%, specificity 90%, precision 90.43%, recall 94.44%, F1-score 92.39% and G-mean 92.19% as compared to the current state-of-the-art techniques. The performance metrics evidenced the use of this model to assist the clinicians for automatic accurate diagnosis of SCZ.