Abstract
A leading cause of dementia, Alzheimer’s disease (AD) affects the cerebral cortex and worsens with time. It’s a debilitating neurological disease that develops progressively over time. The death of brain cells in Alzheimer’s disease causes memory loss and cognitive decline. Preventive steps can be taken by the patient to prevent illness. Creating a tracking and reminder system for Alzheimer’s patients helps them to complete routine tasks. Alzheimer’s disease (AD) and mild cognitive impairment (MCI) have long been diagnosed in patients with neuro-pathological illnesses using neuro imaging. Recent advancement in this area is using multimodal system together with advanced machine learning algorithm to automate the identification and prediction of the progression in Alzheimer disease. This survey focuses on a comprehensive assessment of categorization methodologies and their analytical approaches for predicting Alzheimer disease progression. Also several exhortations for succeeding research in Alzheimer illness have been advised based on the new technology. Along with multimodal diagnosis in the proposed method we will include eye movement tracking, voice analysing and face reading techniques to help in self-evaluation to identify the different stage in the disease.
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Noorul Julaiha, A., Priyatharshini, R. (2022). A Study on Automatic Detection of Alzheimer’s Disease Using Multimodalities. In: Rathore, V.S., Sharma, S.C., Tavares, J.M.R., Moreira, C., Surendiran, B. (eds) Rising Threats in Expert Applications and Solutions. Lecture Notes in Networks and Systems, vol 434. Springer, Singapore. https://doi.org/10.1007/978-981-19-1122-4_66
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