Attention-deficit/hyperactivity disorder (ADHD) is one of the most common and controversial diseases in paediatric psychiatry. Recently, computer-aided diagnosis methods become increasingly popular in clinical diagnosis of ADHD. In this paper, we introduced the latest powerful method—deep
convolutional neural networks (CNNs). Some data augmentation methods and CNN transfer learning technique were used to address the application problem of deep CNNs in the ADHD classification task, given the limited annotated data. In addition, we previously encoded all gray-scale images into
3-channel images via two image enhancement methods to leverage the pre-trained CNN models designed for 3-channel images. All CNN models were evaluated on the published testing dataset from the ADHD-200 sample. Evaluation results show that our proposed deep CNN method achieves a state-of-the-art
accuracy of 66.67% by using data augmentation methods and CNN transfer learning technique, and outperforms existing methods in the literature. The result can be improved by building a special CNN structure. Furthermore, the trained deep CNN model can be used to clinically diagnose ADHD in
real-time. We suggest that the use of CNN transfer learning and data augmentation will be an effective solution in the application problem of deep CNNs in medical image analysis.
