Cicek, G.Ozmen, A.Akan, A.2023-10-192023-10-19201909781728124209https://doi.org/10.1109/TIPTEKNO.2019.8895056https://hdl.handle.net/20.500.12469/49362019 Medical Technologies Congress, TIPTEKNO 2019 --3 October 2019 through 5 October 2019 -- --154293Attention 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. © 2019 IEEE.eninfo:eu-repo/semantics/closedAccessAttention deficit hyperactivitiy disorderClassificationConvolutional neural networkOnline data augmentationBiomedical engineeringClassification (of information)ConvolutionDiagnosisMagnetic resonanceMagnetic resonance imagingNeural networksAttention deficitAttention deficit hyperactivity disorderClassification modelsConvolutional neural networkHealth professionalsHyperactivity disorderKnowledge and experienceOnline dataDeep learningConference Object10.1109/TIPTEKNO.2019.88950562-s2.0-85075603587