Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression
Article
Shoeibi, Afshin, Ghassemi, Navid, Khodatars, Marjane, Moridian, Parisa, Khosravi, Abbas, Zare, Assef, Gorriz, Juan M., Chale-Chale, Amir Hossein, Khadem, Ali and Acharya, U. Rajendra. 2023. "Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression." Cognitive Neurodynamics. 17 (6), p. 1501–152. https://doi.org/10.1007/s11571-022-09897-w
| Article Title | Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression |
|---|---|
| ERA Journal ID | 3179 |
| Article Category | Article |
| Authors | Shoeibi, Afshin, Ghassemi, Navid, Khodatars, Marjane, Moridian, Parisa, Khosravi, Abbas, Zare, Assef, Gorriz, Juan M., Chale-Chale, Amir Hossein, Khadem, Ali and Acharya, U. Rajendra |
| Journal Title | Cognitive Neurodynamics |
| Journal Citation | 17 (6), p. 1501–152 |
| Number of Pages | 23 |
| Year | 2023 |
| Publisher | Springer |
| Place of Publication | Netherlands |
| ISSN | 1871-4080 |
| 1871-4099 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/s11571-022-09897-w |
| Web Address (URL) | https://link.springer.com/article/10.1007/s11571-022-09897-w |
| 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; Diagnosis ; Schizophrenia ; CNN-AE; IT2FR; fMRI; GWO |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | The accessible file is the accepted version of the paper. Please refer to the URL for the published version. |
| Byline Affiliations | K. N. Toosi University of Technology, Iran |
| Ferdowsi University of Mashhad, Iran | |
| Islamic Azad University, Iran | |
| Deakin University | |
| University of Granada, Spain | |
| School of Mathematics, Physics and Computing | |
| Asia University, Taiwan | |
| Singapore University of Social Sciences (SUSS), Singapore |
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https://research.usq.edu.au/item/z1w21/automatic-diagnosis-of-schizophrenia-and-attention-deficit-hyperactivity-disorder-in-rs-fmri-modality-using-convolutional-autoencoder-model-and-interval-type-2-fuzzy-regression
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