ADHD Classification With Low-Frequency Fluctuation Feature Map Based on 3D CBAMe

Abstract

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in teenagers. Some excellent ADHD automatic diagnosis system extracted features from magnetic resonance image (MRI). Researchers have shown fMRI data offers specific measure of ADHD brain activity. In this paper, we propose a low-frequency fluctuation feature map generation approach for ADHD diagnosis, which can highlight the discriminative parts of fMRI features. However, the extracted feature maps still have redundant information. So we add the attention mechanism which can pay more attention to the local information. In order to successfully apply the attention mechanism to convolutional neural network (CNN) and match it to 3D fMRI feature maps, we extend convolutional block attention module (CBAM) from 2D plane to 3D geometric space. After that, we design a single modality 3D CNN based on 3D CBAM to diagnosis ADHD via low-frequency fluctuation feature map. Our model is evaluated on ADHD-200 dataset and it obtains the state-of-the-art classification accuracy of 75.83%. At the same time, our model also simplifies the feature extraction module and the classification module of multi-modality method.