Automatic Diagnosis Research Of Attention Deficit Hyperactivity Disorder Based On Deep Convolutional Neural Network

Attention deficit hyperactivity disorder(ADHD),one of the most common and controversial diseases in paediatric psychiatry,seriously affects the lives of patients and their families.Hence,early diagnosis and treatment are important to patients.The existing diagnostic method based on questionnaires lacks the objective and quantitative diagnostic criteria.Recently,deep learning has achieved remarkable success in many computer vision tasks and is widely used in medical image diagnosis.As a typical deep learning model,convolutional neural network(CNN)can extract hierarchical image features.Based on the abnormalities in brain magnetic resonance imaging,three CNN-based ADHD automatic diagnostic methods are proposed.The main works in the paper list as follow:(1).A single channel ADHD image automatic diagnosis method based on CNN-SVM is proposed.Given the tiny abnormalities,the advantages of CNN and support vector machine(SVM)is combined in the paper,SVM is used to classify the hierarchical features extracted by CNN to realize the automatic diagnosis of ADHD.Experiments were conducted on a subset of the ADHD-200 sample.The cross-validation results show that features learned by the CNN significantly outperform conventional handcrafted features.(2).A three channel ADHD image automatic diagnosis method based on transfer learning and deep CNN is proposed.Given the limited annotated data,application of deep CNN in medical image analysis is a challenge.In this paper,two image enhancement methods are used to encode single channel ADHD images into three channel images to leverage the input requirements of the pre-training model,and the application of deep CNN in ADHD diagnosis is realized by introducing CNN transfer learning techniques and data augmentation methods.Experiments were conducted on the entire ADHD-200 sample.The experimental results show that the CNN transfer learning techniques and data augmentation methods used in this paper can effectively avoid the occurrence of over-fitting,and the proposed algorithm has reached a state-of-the-art accuracy.(3).A multi-channel ADHD image diagnosis method based on 3D CNN is proposed.According to the context information of the 3D image,a 3D CNN for extracting deep space information of multi-channel ADHD images is proposed.Experimental results show that 3D CNN can accurately diagnose ADHD without excessive and complicated preprocessing of medical images.In summary,the proposed method can accurately and objectively and quickly realize the automatic diagnosis of ADHD,which has clinical application value.