Abstract
Alzheimer Disease (AD) is progressive mental deterioration disease results in memory loss issues. Alzheimer is not treatable at the severe stage, but an accurate and earlier identification can aid signs and have great clinical significance. In this chapter, Machine Learning techniques are utilized for the identification purpose for Alzheimer patients. The proposed method extract features from Magnetic Resonance Images (MRI) without segmentation. An accuracy of 94.2% is attained for multiclass classification using random forest approach. The results prove that there is no need for segmentation and hence the process gets robust while keeping the accuracy high. The proposed method is evaluated on OASIS dataset. The computational time for Alzheimer detection is 205 ms and 56 ms for segmentation-based detection and the proposed method respectively.
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Acknowledgement
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A1A09919551).
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Afzal, S., Javed, M., Maqsood, M., Aadil, F., Rho, S., Mehmood, I. (2019). A Segmentation-Less Efficient Alzheimer Detection Approach Using Hybrid Image Features. In: Singh, A., Mohan, A. (eds) Handbook of Multimedia Information Security: Techniques and Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-15887-3_20
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DOI: https://doi.org/10.1007/978-3-030-15887-3_20
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