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    Deep Learning Approach Versus Traditional Machine Learning for ADHD Classification
    (IEEE, 2021) Cicek, Gulay; Akan, Aydin
    Magnetic 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.
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    Deep learning-based computer-aided diagnosis system for attention deficit hyperactivity disorder classification using synthetic data
    (CRC Press, 2022) Cicek, Gulay; Akan, Aydin
    Attention Deficit Hyperactivity Disorder (ADHD) is a neuropsychiatric disorder that affects children and adults. The fact that ADHD symptoms differ from individual to individual, that similar symptoms are seen in other psychiatric diseases, and that the tests used do not contain objectivity are important ob- stacles to the correct diagnosis of the disease. It is inevitable to develop robust and reliable tools for the diagnosis of psychiatric diseases such as physical diseases. The role of neuroimaging techniques in the realization of such a robust tool is undeniable. In this study, deep learning-based ADHD classification models were developed with structural MR data. Synthetic data were obtained with online data augmentation techniques. Different data sets were modeled with AlexNet, VggNet, ResNet, SqueezeNet architectures as well as CNN architectures that we developed. The accuracy rate of our architecture, which has a much shorter training period, is over 90% © 2023 Şaban öztürk. All rights reserved.
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    The Effect of Data Augmentation on ADHD Diagnostic Model using Deep Learning
    (IEEE, 2019) Cicek, Gulay; Ozmen, Atilla; Akan, Aydin
    Attention 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.