16 research outputs found
Automatic Diagnosis of Schizophrenia and Attention Deficit Hyperactivity Disorder in rs-fMRI Modality using Convolutional Autoencoder Model and Interval Type-2 Fuzzy Regression
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 (DL) method. The University of California Los Angeles (UCLA) dataset,
which contains the rs-fMRI modalities of SZ and ADHD patients, has been used
for experiments. The FMRIB software library (FSL) toolbox first performed
preprocessing on rs-fMRI data. Then, a convolutional Autoencoder (CNN-AE) 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
(GA), particle swarm optimization (PSO), and gray wolf optimization (GWO)
techniques. Also, the results of IT2FR methods are compared with multilayer
perceptron (MLP), k-nearest neighbors (KNN), support vector machine (SVM),
random forest (RF), decision tree (DT), and adaptive neuro-fuzzy inference
system (ANFIS) 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
Control degrees of freedom using the restraining number: Further evaluation
10.1002/apj.117Asia-Pacific Journal of Chemical Engineering36638-64