Abstract
Early Alzheimer’s Disease (AD) is essential so that may take a preventive step. Current AD detection techniques depend on checks for cognitive impairment that regrettably now no longer offer an accurate prognosis until the affected person has superior beyond a moderate AD. An incorporated and correct gadget for the prognosis and class of mind issues has consequently been suggested. A smartphone is used as a tool to gather sensor datasets. Another hardware tool used is computer and software tools such as python IDE, R studio, and Anaconda. This paper depicts a detailed literature survey on various techniques for identifying AD from the brain image dataset. This survey will help researchers to give direction to their work in this domain. This paper gives a brief idea about various machine learning algorithms and challenges faced during the work. The AD is successfully detected and classified as a result.
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Mane, D.T., Patel, M., Sawant, M., Maiyani, K., Patil, D. (2022). A Survey on Alzheimer’s Disease Detection and Classification. In: Nanda, P., Verma, V.K., Srivastava, S., Gupta, R.K., Mazumdar, A.P. (eds) Data Engineering for Smart Systems. Lecture Notes in Networks and Systems, vol 238. Springer, Singapore. https://doi.org/10.1007/978-981-16-2641-8_60
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