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    Automated Segmentation of Gray and White Matter Regions in Brain MRI Images for Computer Aided Diagnosis of ADHD
    (Institute of Electrical and Electronics Engineers Inc., 2023) Cicek G.; Akan A.
    Attention deficit hyperactivity (ADHD) is a psychiatric disorder that affects millions of children and many times last into adulthood. There is no single test that can show whether a person has ADHD. The symptoms of ADHD vary from person to person. Therefore it is hard to diagnose ADHD contrary many physical illness. Our aim is to create methods to minimize human effort and increase accurate of diagnosis of attention deficit hyperactivity disorder. So, we collected Structural Magnetic Resonance Imaging (MRI) from 26 subjects: 11 controls and 15 children diagnosed with ADHD. The data was provided from NPIstanbul NeuroPsyhiatric Hospital. We used k-means clustering algorithm to extract gray matter and white matter from the axial plane. Four features is extracted from these region; area of gray matter, area of white matter and perimeter of gray matter, perimeter of white matter. The most important attribute was determined by using principal component analysis. The models were built on the k-nearest neighbors algorithm (knn) and decision tree using Matlab machine learning toolbox. The experiments were conducted on a full training dataset including 26 instance and 5 fold cross validation was adopted for randomly sampling training and test set. The outcome of our study will reduce the number medical errors by informing physicians in their determination of diagnosing of attention deficit hyperactivity disorder. These method we used classifies ADHD successfully up to % 91 accuracy. © 2023 IEEE.
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    Machine and Deep Learning Based Detection of Attention Deficit Hyperactivity Disorder
    (Institute of Electrical and Electronics Engineers Inc., 2023) Cicek G.; Akan A.
    Attention Deficit Hyperactivity Disorder (ADHD) is a brain disease that can cause academic, social and psychiatric problems. Structural Magnetic resonance imaging (structural MR) is a critical diagnostic tool used to examine brain anatomy and pathology. In this study, an objective ADHD detection model was developed with Machine Learning (ML) and Deep Learning (DL) methods using structural MR images. Gray and white matter is an important parameter in the diagnosis of many psychiatric diseases. An algorithm has been developed for the detection of slices in which gray and white matter appear complete and clear. While the slices determined by the proposed algorithm are assigned to one dataset, all slices of the structural MR image are assigned to the other dataset. Different feature sets were created by characterizing structural MR images with ML (LBP and Haralick) and DL methods (AlexNet, VggNet, ResNet, SqueezeNet and InceptionResNet). High classification performances were observed in the characterization of the dataset containing the selected slices with ML and DL algorithms. High classification performances were observed with LBP and Haralick, which were especially successful in capturing changes in texture. © 2023 IEEE.