Abstract
Due to 2D-CNN cannot effectively use the continuous change information in MRI to classify Alzheimer’s disease (AD), an MDLCSTM-LDenseNet model based on multi-scale features and sequence learning is proposed. On the basis of retaining the advantages of DenseNet, 3D Light-DenseNet with fewer parameters is given as the basic network, and the MDLCSTM module combining dilated convolution and ConvLSTM is embedded in the 3D Light-DenseNet to further extract the slice features and the continuous change information between slice sequences in the global slice range of MRI. Based on the experimental of MRI data in ADNI database with other methods, the classification accuracy of AD and CN is 97.25%, and the classification accuracy of CN and MCI is 92.97%. The results show that the model has a high classification accuracy and reliability.
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Han, S., Wang, L., Song, D. (2021). Research on Classification of Alzheimer’s Disease Based on Multi-scale Features and Sequence Learning. In: Liang, Q., Wang, W., Liu, X., Na, Z., Li, X., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2020. Lecture Notes in Electrical Engineering, vol 654. Springer, Singapore. https://doi.org/10.1007/978-981-15-8411-4_252
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DOI: https://doi.org/10.1007/978-981-15-8411-4_252
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