Abstract
Nowadays, an increasing number of people are facing the consequences of late detection of neurodegenerative diseases. There is an evident rise in the incidence of these diseases due to the ageing of the population. Alzheimer’s disease is one of the most common neurodegenerative diseases, making it essential to develop a practical diagnostic method. In this article, an original method for detecting Alzheimer’s disease using deep learning techniques was proposed based on the analysis of data originating from multiple modalities: magnetic resonance imaging (MRI) scans, blood test results, and psychological tests. The method was implemented, examined, and compared to existing solutions-the development of the method utilized available patient data from the ADNI dataset. The research results confirmed that the proposed method for Alzheimer’s disease detection could be a promising diagnostic tool. It exhibited high accuracy in identifying cases of Alzheimer’s disease and was able to distinguish these cases from both healthy individuals and those with mild cognitive impairment (MCI).
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Kiciński, K., Pietranik, M., Kozierkiewicz, A. (2024). A New Method of Detecting Alzheimer’s Disease. In: Nguyen, N.T., et al. Computational Collective Intelligence. ICCCI 2024. Lecture Notes in Computer Science(), vol 14811. Springer, Cham. https://doi.org/10.1007/978-3-031-70819-0_24
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DOI: https://doi.org/10.1007/978-3-031-70819-0_24
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