Abstract
The usual computer aided diagnosis approaches of Alzheimer’s disease patients based on fMRI often require a lot of manual intervention. By contrast, H-ELM needs only less manual intervention and can extract features by a multi-layer feature representation framework. Therefore, an AD CADx model based on H-ELM is proposed. First, the common spatial pattern is used to extract information from the BOLD signals, and then the features are encoded and trained by H-ELM. H-ELM is used to realize the expression of deep feature of the brain, so as to further improve the diagnostic accuracy. Finally, experimental evaluation proved the effectiveness of the proposed algorithms.
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Acknowledgments
This research was partially supported by the National Natural Science Foundation of China (Nos. 61472069, 61402089 and U1401256), the Fundamental Research Funds for the Central Universities (Nos. N161602003, N171607010, N161904001, and N160601001), the Natural Science Foundation of Liaoning Province (No. 2015020553).
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Wang, Z., Xin, J., Zhao, Y., Guo, Q. (2020). Alzheimer’s Disease Computer Aided Diagnosis Based on Hierarchical Extreme Learning Machine. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM 2018. ELM 2018. Proceedings in Adaptation, Learning and Optimization, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-23307-5_5
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DOI: https://doi.org/10.1007/978-3-030-23307-5_5
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