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dc.contributor.authorÇiçek, Gülay
dc.contributor.authorÖzmen, Atilla
dc.contributor.authorAkan, Aydin
dc.description.abstractAttention Deficit Hyperactivity Disorder (ADHD) is a neuro-behavioral hyperactivity disorder. It is frequently seen in childhood and youth, and lasts a lifetime unless treated.The ADHD classification model should be objective and robust. Correct diagnosis usually depends on the knowledge and experience of health professionals. In this respect, an automated method to be developed for the ADHD classification model is of great importance for clinicians. In this study, the effect of data augmentation on ADHD classification model with deep learning was investigated. For this purpose, magnetic resonance images were taken from NPIstanbul NeuroPsychiatry Hospital and ADHD-200 database. Since the images were not sufficient in terms of training, data augmentation methods were applied and by convolutional neural network (CNN) architecture, these data were classified and tried to reveal the diagnosis of the disease independently from the non-objective experiences of the health professionals.en_US
dc.subjectOnline data augmentationen_US
dc.subjectConvolutional neural networken_US
dc.subjectAttention deficit hyperactivitiy disorderen_US
dc.titleThe Effect of Data Augmentation on ADHD Diagnostic Model using Deep Learningen_US
dc.typeProceedings Paperen_US
dc.relation.journal2019 Medical Technologies Congress (Tiptekno)en_US
dc.contributor.khasauthorÖzmen, Atillaen_US

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