Automated Segmentation of Gray and White Matter Regions in Brain MRI Images for Computer Aided Diagnosis of ADHD

dc.contributor.authorCicek G.
dc.contributor.authorAkan A.
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description2023 Medical Technologies Congress, TIPTEKNO 2023 -- 10 November 2023 through 12 November 2023 -- -- 195703en_US
dc.description.abstractAttention 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.en_US
dc.description.sponsorship2017-ÖNAP-MÜMF-0002, 2019-GAP-MÜMF-003en_US
dc.description.sponsorshipFUNDING This work was supported by Izmir Katip Celebi University Scientific Research Projects Coordination Unit: Project numbers 2019-GAP-MÜMF-003 and 2017-ÖNAP-MÜMF-0002.en_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofTIPTEKNO 2023 - Medical Technologies Congress, Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.subjectdecision treeen_US
dc.subjectgray and white matteren_US
dc.subjectk-nearest neighboren_US
dc.titleAutomated Segmentation of Gray and White Matter Regions in Brain MRI Images for Computer Aided Diagnosis of ADHDen_US
dc.typeConference Objecten_US