Abstract
Convolutional neural networks, combined with attention mechanisms, can effectively extract global and local features from structural magnetic resonance images to aid in the diagnosis of Alzheimer’s disease (AD). However, the attention mechanism still presents challenges in AD diagnosis. First, channel attention degradation and feature correction processes lead to the loss of important features. Second, capturing directional information during spatial attention correction is difficult. Therefore, this study proposes the Spatial and Channel attention Network (SCANet) based on Gated Residual Channel Attention (GRCA) and Spatial Asymmetric Attention (SAS) blocks. The GRCA block is based on the normalized attention jumping mechanism, which reduces attentional decay that occurs when the network is too deep, and the block can be calibrated to further enhance important features and suppress non-important features. The SAS block uses asymmetric convolution to model the horizontal and vertical direction-related attention information generated by different pooling methods. It adopts a cross-fertilization strategy to fuse the attention direction information generated by different asymmetric convolutions with different pooling methods, obtaining an attention vector with direction information. The SCANet model was validated through various experiments. The results of five-fold cross-validation showed that SCANet has an average classification accuracy of 97.84% for AD and normal control, which is better than that of the comparison models.
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References
Abdelaziz, M., Wang, T., Elazab, A.: Alzheimer’s disease diagnosis framework from incomplete multimodal data using convolutional neural networks. J. Biomed. Inform. 121, 103863 (2021). https://doi.org/10.1016/j.jbi.2021.103863
Benavoli, A., Corani, G., Mangili, F.: Should we really use post-hoc tests based on mean-ranks? J. Mach. Learn. Res. 17(1), 152–161 (2016)
Chan, H.P., Hadjiiski, L.M., Samala, R.K.: Computer-aided diagnosis in the era of deep learning. Med. Phys. 47(5), e218–e227 (2020). https://doi.org/10.1002/mp.13764
da Silveira Souza, B., Poloni, K.M., Ferrari, R.J.: Detector of 3-D salient points based on the dual-tree complex wavelet transform for the positioning of hippocampi meshes in magnetic resonance images. J. Neurosci. Methods 341, 108789 (2020). https://doi.org/10.1016/j.jneumeth.2020.108789
Demiar, J., Schuurmans, D.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7(1), 1–30 (2006)
Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., Sun, J.: Repvgg: making VGG-style convnets great again. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13728–13737 (2021). https://doi.org/10.1109/CVPR46437.2021.01352
Feng, J., Zhang, S.W., Chen, L., Xia, J.: Alzheimer’s disease classification using features extracted from nonsubsampled conto***et subband-based individual networks. Neurocomputing 421, 260–272 (2021). https://doi.org/10.1016/j.neucom.2020.09.012
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90
Howard, A.G., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications. CoRR abs/1704.04861 (2017)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018). https://doi.org/10.1109/CVPR.2018.00745
Khan, N.M., Abraham, N., Hon, M.: Transfer learning with intelligent training data selection for prediction of Alzheimer’s disease. IEEE Access 7, 72726–72735 (2019). https://doi.org/10.1109/ACCESS.2019.2920448
Li, H., et al.: Attention-based and micro designed EfficientNetB2 for diagnosis of Alzheimer’s disease. Biomed. Signal Process. Control 82, 104571 (2023). https://doi.org/10.1016/j.bspc.2023.104571
Li, K., et al.: Gray matter structural covariance networks changes along the Alzheimer’s disease continuum. NeuroImage: Clin. 23, 101828 (2019). https://doi.org/10.1016/j.nicl.2019.101828
Lian, C., Liu, M., Zhang, J., Shen, D.: Hierarchical fully convolutional network for joint atrophy localization and Alzheimer’s disease diagnosis using structural mri. IEEE Trans. Pattern Anal. Mach. Intell. 42(4), 880–893 (2020). https://doi.org/10.1109/TPAMI.2018.2889096
Liu, J., Pan, Y., Wu, F.X., Wang, J.: Enhancing the feature representation of multi-modal MRI data by combining multi-view information for MCI classification. Neurocomputing 400, 322–332 (2020). https://doi.org/10.1016/j.neucom.2020.03.006
Ma, N., Zhang, X., Zheng, H.T., Sun, J.: Shufflenet v2: practical guidelines for efficient CNN architecture design. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 116–131 (2018)
Marcus, D.S., Fotenos, A.F., Csernansky, J.G., Morris, J.C., Buckner, R.L.: Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults. J. Cogn. Neurosci. 22(12), 2677–2684 (2010). https://doi.org/10.1162/jocn.2009.21407
Mueller, S.G., et al.: The Alzheimer’s disease neuroimaging initiative. Neuroimaging Clin. N. Am. 15(4), 869–877 (2005). https://doi.org/10.1016/j.nic.2005.09.008
Peng, Z., Huang, W., Gu, S., Xie, L., Wang, Y., Jiao, J., Ye, Q.: Conformer: local features coupling global representations for visual recognition (2021). https://doi.org/10.1109/ICCV48922.2021.00042
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)
Small, G.W., Greenfield, S.: Current and future treatments for Alzheimer disease. Am. J. Geriatr. Psychiatry 1101–1105 (2015)
So, J.H., Madusanka, N., Choi, H.K., Choi, B.K., Park, H.G.: Deep learning for Alzheimers disease classification using texture features. Curr. Med. Imaging Rev. 15(7), 689–698 (2019). https://doi.org/10.2174/1573405615666190404163233
Sun, Z., Qiao, Y., Lelieveldt, B.P., Staring, M.: Integrating spatial-anatomical regularization and structure sparsity into SVM: improving interpretation of Alzheimer’s disease classification. Neuroimage 178, 445–460 (2018). https://doi.org/10.1016/j.neuroimage.2018.05.051
Wan, L., Zeiler, M., Zhang, S., Le Cun, Y., Fergus, R.: Regularization of neural networks using dropconnect. In: International Conference on Machine Learning, pp. 1058–1066. PMLR (2013)
Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: ECA-Net: efficient channel attention for deep convolutional neural networks. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11531–11539 (2020). https://doi.org/10.1109/CVPR42600.2020.01155
Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: CBAM: convolutional block attention module (2018)
Wu, Y., Zhou, Y., Zeng, W., Qian, Q., Song, M.: An attention-based 3D CNN with multi-scale integration block for Alzheimer’s disease classification. IEEE J. Biomed. Health Inform. 26(11), 5665–5673 (2022). https://doi.org/10.1109/JBHI.2022.3197331
Yan, B., et al.: Quantifying the impact of pyramid squeeze attention mechanism and filtering approaches on Alzheimer’s disease classification. Comput. Biol. Med. 148, 105944 (2022). https://doi.org/10.1016/j.compbiomed.2022.105944
Yang, Z., Zhu, L., Wu, Y., Yang, Y.: Gated channel transformation for visual recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11794–11803 (2020)
Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848–6856 (2018)
Zhao, X., Ang, C.K.E., Acharya, U.R., Cheong, K.H.: Application of artificial intelligence techniques for the detection of Alzheimer’s disease using structural MRI images. Biocybern. Biomed. Eng. 41(2), 456–473 (2021). https://doi.org/10.1016/j.bbe.2021.02.006
Zhu, K., Wu, J.: Residual attention: a simple but effective method for multi-label recognition. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 184–193 (2021). https://doi.org/10.1109/ICCV48922.2021.00025
Zhu, W., Sun, L., Huang, J., Han, L., Zhang, D.: Dual attention multi-instance deep learning for Alzheimer’s disease diagnosis with structural MRI. IEEE Trans. Med. Imaging 40(9), 2354–2366 (2021). https://doi.org/10.1109/TMI.2021.3077079
Acknowledgements
This work was supported by a grant from the National Natural Science Foundation of China (82074003, 82174083), Chinese medicine (ethnic medicine) frontier research and development innovation team of Sichuan Administration of Traditional Chinese Medicine (No. 2022C010), the Sichuan Provincial Program of Traditional Chinese Medicine of China (2024ZD014), the Science and Technology Project in Sichuan (2022NSFSC0507, 2024YFFK0362), and the Fundamental Research Funds for the Central Universities of China, Southwest Minzu University (ZYN2023098).
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Wu, D. et al. (2024). SCANet: Dual Attention Network for Alzheimer’s Disease Diagnosis Based on Gated Residual and Spatial Asymmetry Mechanisms. In: Wand, M., Malinovská, K., Schmidhuber, J., Tetko, I.V. (eds) Artificial Neural Networks and Machine Learning – ICANN 2024. ICANN 2024. Lecture Notes in Computer Science, vol 15023. Springer, Cham. https://doi.org/10.1007/978-3-031-72353-7_28
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