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Information Journal Paper

Title

RESTING STATE FMRI ANALYSIS USING PSO ALGORITHM TO DIAGNOSE ADHD DISEASE

Pages

  53-66

Abstract

 Studying the resting state functional magnetic resonance imaging (rs-fMRI) has been an important issue for diagnosing of the diseases related to brain function, nowadays. This paper proposes a novel method for diagnosis of the ATTENTION-DEFICIT/HYPERACTIVITY DISORDER (ADHD) diseases that includes three main modules. The first module is the feature extraction that extracts the correlation between the time series of the signals of the different brain regions. In the second module the SVM is used as the classifier. The third module is an optimizatrion algorithm, i.e. PARTICLE SWARM OPTIMIZATION ALGORITHM (PSO). PSO chooses the appropriate features by changing the length of its particles. The results show that the proposed method has high classification accuracy in comparison with others.

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  • Cite

    APA: Copy

    YOSEFI, MEHDI, EBRAHIMZADE, ATAOLLAH, KHAZAEI, ALI, & BABAJANI, ABBAS. (2012). RESTING STATE FMRI ANALYSIS USING PSO ALGORITHM TO DIAGNOSE ADHD DISEASE. JOURNAL OF SOFT COMPUTING AND INFORMATION TECHNOLOGY (JSCIT), 1(3), 53-66. SID. https://sid.ir/paper/245886/en

    Vancouver: Copy

    YOSEFI MEHDI, EBRAHIMZADE ATAOLLAH, KHAZAEI ALI, BABAJANI ABBAS. RESTING STATE FMRI ANALYSIS USING PSO ALGORITHM TO DIAGNOSE ADHD DISEASE. JOURNAL OF SOFT COMPUTING AND INFORMATION TECHNOLOGY (JSCIT)[Internet]. 2012;1(3):53-66. Available from: https://sid.ir/paper/245886/en

    IEEE: Copy

    MEHDI YOSEFI, ATAOLLAH EBRAHIMZADE, ALI KHAZAEI, and ABBAS BABAJANI, “RESTING STATE FMRI ANALYSIS USING PSO ALGORITHM TO DIAGNOSE ADHD DISEASE,” JOURNAL OF SOFT COMPUTING AND INFORMATION TECHNOLOGY (JSCIT), vol. 1, no. 3, pp. 53–66, 2012, [Online]. Available: https://sid.ir/paper/245886/en

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