Image Classification of Alzheimer's Disease based on Residual Bilinear and Attentive Models

Abstract

Due to the characteristics of high noise and low resolution in medical images, it is difficult to extract local features, which affects the accuracy of image diagnosis and classification. To exploit the discriminative features of local image regions, we propose a network model method that combines improved residual bilinear and attention mechanism. First, in the ResNeXt model, it performs segmentation and convolution on the original residual unit structure to extract multi-scale features of the image. And it replaces the VGGNet model in bilinear. Then, it uses channel nonlinear attention to obtain expressive features when extracting features, and employs spatial attention for weight region selection to achieve BAP (Bilinear Attention Pooling) fusion. Finally, it implements classification in the SVM classifier and tests our model on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The results show that the model has better accuracy and robustness than other models in AD diagnosis classification.