Abstract
Alzheimer’s disease is a kind of chronic disease, and its early detection have a positive impact on the outcome of subsequent treatment. However, relying entirely on manual diagnosis is time-consuming and requires high expertise. Therefore, computer-aided diagnosis becomes very urgent. Considering the wide application of deep learning in computer-aided diagnosis, a method based on deep learning to assist in the diagnosis of Alzheimer’s disease was proposed in this paper. Specifically, this method started with brain extraction on the original sMRI images, and selected some sMRI images slices from the extracted images. Then it used ResNet and Attention network to extract features from the selected sMRI images slices. Finally, the extracted features were fed into the fully connected network for classification. We designed two ablation experiments and one comparative experiment. The results of the ablation experiments showed the effects of sMRI images slice numbers and Attention neural network on our method. Comparative experiment result showed that the Accuracy and Specificity of the proposed method reach 95.33\(\%\), 97.38\(\%\) respecitively, which were better than state-of-the-art methods.
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Xing, Y., Guan, Y., Yang, B., Liu, J. (2022). Classification of sMRI Images for Alzheimer’s Disease by Using Neural Networks. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13535. Springer, Cham. https://doi.org/10.1007/978-3-031-18910-4_5
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