Deep Learning-Based Binary Classification of ADHD Using Resting State MR Images

Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neuropsychiatric disorders in adolescence and adult, but the origin of this disorder is still under research. The focus of this paper is on classification of resting state functional magnetic resonance imaging (rs-fMRI) of ADHD and healthy controls using deep learning techniques. ADHD-200 dataset includes resting state rs-fMRI images of ADHD, and typically developing controls and deep learning-based techniques such as 2-dimensional convolutional neural network (CNN) algorithm and hybrid 2-dimensional convolutional neural network–long short-term memory (2D CNN–LSTM) were applied on this dataset for the classification of ADHD from typically developing controls. The proposed hybrid system evaluated on the basis of parameters, viz. accuracy, specificity, sensitivity, F1-score, and AUC. In comparison with existing methods, the proposed method achieved significant improvement in analyzing and detection of parameters. By incorporating techniques of deep learning with rs-fMRI, the results built up an adequate and intelligent model to comparatively diagnose ADHD from healthy controls.