Automated Classification of Alzheimer’s Disease Stages Using T1-Weighted sMRI Images and Machine Learning
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-5180-2_28
Reference21 articles.
1. Chen, Y., Xia, Y.: Iterative sparse and deep learning for accurate diagnosis of Alzheimer’s disease. Pattern Recognit. 116, 107944 (2021). https://doi.org/10.1016/j.patcog.2021.107944
2. Zhang, J., Zheng, B., Gao, A., et al.: A 3D densely connected convolution neural network with connection-wise attention mechanism for Alzheimer’s disease classification. Magn Reson Imaging 78, 119–126 (2021). https://doi.org/10.1016/j.mri.2021.02.001
3. Lao, H., Zhang, X.: Regression and classification of Alzheimer’s disease diagnosis using NMF-TDNet features from 3D brain MR Image. IEEE J Biomed. Heal. Inform. 26, 1103–1115 (2022). https://doi.org/10.1109/JBHI.2021.3113668
4. Goenka, N., Tiwari, S.: AlzVNet: a volumetric convolutional neural network for multiclass classification of Alzheimer’s disease through multiple neuroimaging computational approaches. Biomed. Signal Process Control 74, 103500 (2022). https://doi.org/10.1016/j.bspc.2022.103500
5. Vishnu, V.Y., Modi, M., Garg, V.K., et al.: Role of inflammatory and hemostatic biomarkers in Alzheimer’s and vascular dementia—a pilot study from a tertiary center in Northern India. Asian J Psychiatr 29, 59–62 (2017). https://doi.org/10.1016/j.ajp.2017.04.015
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