Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression

TL;DR

This paper introduces a novel deep learning approach using convolutional autoencoders and interval type-2 fuzzy regression, optimized by metaheuristics, for diagnosing schizophrenia and ADHD from rs-fMRI data, achieving 72.71% accuracy.

Contribution

It proposes a new combination of convolutional autoencoder features and fuzzy regression with optimization algorithms for brain disorder diagnosis from rs-fMRI data.

Findings

The GWO-optimized IT2FR method outperformed other classifiers.

Achieved 72.71% accuracy in distinguishing SZ and ADHD.

Demonstrated effectiveness of deep learning and fuzzy regression in medical diagnosis.

Abstract

Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy.