Abstract
Alzheimer’s disease (AD) is an incurable neurodegenerative disease which is one of the leading causes of death in elderly people. Early and accurate detection of AD is vital for appropriate treatment. AI-based automated techniques are widely used to help early diagnosis of AD. In recent years, machine learning and deep learning has become the preferred method of analyzing medical images, and it has also attracted a high degree of attention in AD detection. Researchers have proposed many novel approaches for automated detection and gradation of the disease. The success of any such approach depends on the appropriate selection of pre-processing, biomarkers, feature extraction, and model architecture. This paper presents a review of the efficacy of different methods used by the researchers for these components with the aim to understand the state-of-the-art architecture. A comparative analysis of their advantages, disadvantages, and performance accuracy is reported.
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Bhadra, S.R., Sengupta, S. (2024). A Review on Machine Learning and Deep Learning Based Approaches in Detection and Grading of Alzheimer’s Disease. In: Dasgupta, K., Mukhopadhyay, S., Mandal, J.K., Dutta, P. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2023. Communications in Computer and Information Science, vol 1955. Springer, Cham. https://doi.org/10.1007/978-3-031-48876-4_1
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