Xiabin Zhang1,2, Zhongyi Hu1,2,*, Lei Xiao1,2, Hui Huang1,2
CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2879-2905, 2024, DOI:10.32604/cmc.2024.048725
Abstract Various deep learning models have been proposed for the accurate assisted diagnosis of early-stage Alzheimer’s disease (AD). Most studies predominantly employ Convolutional Neural Networks (CNNs), which focus solely on local features, thus encountering difficulties in handling global features. In contrast to natural images, Structural Magnetic Resonance Imaging (sMRI) images exhibit a higher number of channel dimensions. However, during the Position Embedding stage of Multi Head Self Attention (MHSA), the coded information related to the channel dimension is disregarded. To tackle these issues, we propose the RepBoTNet-CESA network, an advanced AD-aided diagnostic model that is capable… More >
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