fMRI Feature Extraction Model for ADHD Classification Using Convolutional Neural Network
نویسندگان
چکیده
Biomedical intelligence provides a predictive mechanism for the automatic diagnosis of diseases and disorders. With advancements computational biology, neuroimaging techniques have been used extensively in clinical data analysis. Attention deficit hyperactivity disorder (ADHD) is psychiatric disorder, with symptomology inattention, impulsivity, hyperactivity, which early crucial to prevent unwelcome outcomes. This study addresses ADHD identification using functional magnetic resonance imaging (fMRI) resting state brain by evaluating multiple feature extraction methods. The features seed-based correlation (SBC), fractional amplitude low-frequency fluctuation (fALFF), regional homogeneity (ReHo) are comparatively applied obtain specificity sensitivity. helps determine best classification convolutional neural networks (CNN). methodology fALFF ReHo resulted an accuracy 67%, while SBC gained between 84% 86% sensitivity 65% 75%.
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ژورنال
عنوان ژورنال: International Journal of E-health and Medical Communications
سال: 2021
ISSN: ['1947-3168', '1947-315X']
DOI: https://doi.org/10.4018/ijehmc.2021010106