Cicek, G.Cevik, M.Akan, A.2023-10-192023-10-19201909781728124209https://doi.org/10.1109/TIPTEKNO.2019.8895197https://hdl.handle.net/20.500.12469/49372019 Medical Technologies Congress, TIPTEKNO 2019 --3 October 2019 through 5 October 2019 -- --154293Attention Deficit Hyperactivity (ADHD) is a common neurodevelopmental disorder that typically appears in early childhood. Methods developed for diagnosing gives different results at different times. This is a major obstacle in the diagnosis of disease. Diagnosis model of ADHD must be unique, objective, and reliable. In this study, comparative evaluations of both manual and deep features for classification of structural magnetic resonance images is presented. For this purpose, datasets of NPIstanbul Neuropsychiatry Hospital and public datasets of ADHD-200 is used. In order to characterize MRI images First Order, Second Order statictical features and the Alexnet architecture is used. Images are classified with the ensemble algorithm. In order to determine classification performance, accuracy, sensitivity, specificity, tp rate, fp rate and F-measure values are taken into consideration. It was observed that the combination of three manually extracted data sets yielded more successful results in characterizing the data. © 2019 IEEE.eninfo:eu-repo/semantics/closedAccessAlexnetAttention deficit hyperactivity disorderConvolutional neural networkHand crafted and automated featuresBiomedical engineeringDiagnosisMagnetic resonanceMagnetic resonance imagingNeural networksAlexnetAttention deficit hyperactivityAttention deficit hyperactivity disorderAutomated featuresClassification performanceComparative evaluationsConvolutional neural networkEnsemble algorithmsDeep learningConference Object10.1109/TIPTEKNO.2019.88951972-s2.0-85075619561