Deep Learning Approach Versus Traditional Machine Learning for ADHD Classification

dc.contributor.authorCicek, Gulay
dc.contributor.authorAkan, Aydin
dc.departmentİstanbul Beykent Üniversitesien_US
dc.descriptionMedical Technologies Congress (TIPTEKNO'21) -- NOV 04-06, 2021 -- Antalya, TURKEYen_US
dc.description.abstractMagnetic resonance is the imaging method that stands out in the evaluation of textures and diseases related to brain. The information about metabolic, biochemical and hemodynamic structure of the brain is obtained by magnetic resonance imaging. Attention Deficit Hyperactivity Disorder (ADHD) is a psychiatric disease and, if not treated, its effects may spread over all lifetime and cause significant academic, social, and psychiatric problems. High-accuracy and objective tools need to be developed for classification of ADHD. In this study, we present machine learning (ML) and deep learning (DL) based approaches for the classification of MR Images collected from ADHD patients. We generate a new 2D texture from 3-D structural magnetic resonance image by combining slices where gray and white matter clearly displayed. In the first approach, we extract Haralick texture based features, and HOG features and classify ADHD using ML methods such as Decision Tree, K nearest neighbor, Naive Bayes, Logistic Regression, and Support Vector Machine. In the DL approach, we trained four Convolutional Neural Network (CNN) structures (AlexNet, VGGNet, ResNet and GoogleNet) for ADHD classification using the 2-D texture images. Classification performance obtained with ResNet architecture in characterizing new texture is 100 % accuracy, 100 % sensitivity, 100 % specificity.en_US
dc.description.sponsorshipBiyomedikal Klinik Muhendisligi Dernegi,Izmir Ekonomi Univen_US
dc.description.sponsorshipIzmir Katip Celebi University Scientific Research Projects Coordination Unit [2019-GAP-MUMF-003, 2017-ONAP-MUMF-0002]en_US
dc.description.sponsorshipThis work was supported by Izmir Katip Celebi University Scientific Research Projects Coordination Unit: Project numbers 2019-GAP-MUMF-003 and 2017-ONAP-MUMF-0002. We would like to thank Doc.Dr. Baris Metin, Neurology Specialist at NPIstanbul NeuroPsychiatric Hospital for his assistance and for providing MR Data.en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.relation.ispartofTip Teknolojileri Kongresi (Tiptekno'21)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.subjectAttention Deficit Hyperactivity Disorderen_US
dc.subjectstructural magnetic resonance imagingen_US
dc.subjectmachine learningen_US
dc.subjectdeep learningen_US
dc.titleDeep Learning Approach Versus Traditional Machine Learning for ADHD Classificationen_US
dc.typeConference Objecten_US