Deep learning-based computer-aided diagnosis system for attention deficit hyperactivity disorder classification using synthetic data

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.