MSFNet-2SE: A multi-scale fusion convolutional network for Alzheimer's disease classification on magnetic resonance images

Abstract

Alzheimer's disease (AD) is an irreversible neurodegenerative disease, and the early diagnosis and effective intervention of AD is essential for patients and doctors. Intelligence diagnosis based on magnetic resonance imaging and deep learning has become one of the useful methods for AD identification. To improve the diagnosis effect of AD in early stages, a novel multi-scale classification model of AD, called MSFNet-2SE, is proposed. First, a new multi-scale fusion (MSF) feature extraction module is designed based on the idea of the feature maps split into feature subsets of Res2Net. Second, a channel attention module is embedded into the MSF module through integrating two Squeeze-and-Excitation (SE) blocks. Finally, a gradient centralization Adam optimizer is used to improve the model classification performance. Experimental results illustrate that, compared with other available state-of-the-art classification models of AD, the proposed model has excellent classification performance. It is helpful to improve the clinical diagnosis efficiency.