  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.
