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

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.

Keywords: ADHD; CNN-AE; Diagnosis; GWO; IT2FR; Schizophrenia; fMRI.

Fig. 1

Block diagram of the proposed method

Fig. 2

Sample rs-fMRI data from HC subjects (row A) and patients (row B)

Fig. 3

Overview of the preprocessing steps

Fig. 4

Sample images before and after applying BET on raw T1-images

Fig. 5

Sample images after three main preprocessing steps. Row (A): raw rs-fMRI data. Row (B): Raw rs-fMRI data after brain extraction, slice timing correction, and filtering. Row (C): Registered Image to standard space

Fig. 6

Sample correlation matrices obtained for HC subjects

Fig. 7

Sample correlation matrices obtained for SZ subjects

Fig. 8

Sample correlation matrices obtained for ADHD subjects

Fig. 9

Proposed CNN-AE model for diagnosis of SZ from rs-fMRI modality

Fig. 10

AE-based Classifier block diagram

Fig. 11

Gaussian IT2F membership functions

Fig. 12

Upper MFs and lower MFs of Gaussian IT2F

Fig. 13

Block diagram of GWO optimization method

Fig. 14

Confusion matrix for KNN method

Fig. 15

Comparison of performances (%) for SZ and ADHD detection

Fig. 16

Three Gaussian membership functions in ANFIS classifier

Fig. 17

Confusion matrix for ANFIS-GWO

Fig. 18

Comparison of ANFIS classifiers results for SZ and ADHD detection

Fig. 19

Three Gaussian membership functions in IT2FR classifier

Fig. 20

Confusion matrix for IT2FR-GWO

Fig. 21

Comparison of IT2RF classifiers results for SZ and ADHD detection