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• W4379874942 abstract "Throughout the world, at least five crore individuals are thought to have the disease of Alzheimer. This Alzheimer's disease is the most well-known and prevalent kind of dementia (AD). Since the modest but significant advancements in Alzheimer's disease (AD) treatment, the diagnostic emphasis has progressively switched to the precise identification of the disease's earliest stage. In numerous attempts at clinical classification, the difficulty of differentiating pre-clinical AD from changes associated with normal aging or developed AD has been recognized. Alzheimer's disease is a neurological and brain disorder that eventually makes it difficult to do even the most fundamental tasks. It gradually destroys memory and thinking skills. It is a neurological degenerative disorder that causes both brain cell shrinkage and degeneration. The most typical reason of dementia is a gradual loss of cognitive ability, behavioral, and social abilities, limiting their capacity to operate independently. It causes both brain cell shrinkage and degeneration. As people live longer, there are more concerns about aging. Damage to brain cells can be avoided with an early diagnosis of this disease. Early detection can considerably slow or stop the growth of this disease because there is no cure for it. To stop the progression of Alzheimer's disease, early detection is essential. As a result, experts can initiate preventive care as soon as possible. They call for prompt and precise Alzheimer's disease detection in its initial and most elusive stages. The only accurate approach for diagnosis is magnetic resonance imaging (MRI) brain imaging, although exams like the Mini-Mental State Examination (Fol stein 1975), or MMSE, are routinely used for earlier diagnosis. The main objective of this research work is to devise a technique for accurately identifying and staging diseases in magnetic resonance imaging (MRI). Deep learning excels at analyzing images and predicting diseases. A growing body of research suggests that deep learning techniques may be critical during detection and prognosis of the condition of Alzheimer's. Therefore, more research is being done on deep learning models with the highest early detection accuracy. Brain imaging will be used to determine the disease stage in this investigation. Additionally, using MRI data, we would like to propose an efficient model for deep-learning for diagnosing Alzheimer's disease. Here, the dataset is obtained from Kaggle. This research study provides a novel approach of deep learning that makes use of the CNN algorithm to identify the disease of Alzheimer's." @default.
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• W4379874942 date "2023-05-04" @default.
• W4379874942 modified "2023-09-25" @default.
• W4379874942 title "Prediction of Alzheimer's Disease using Deep Learning Algorithms" @default.
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• W4379874942 doi "https://doi.org/10.1109/icaaic56838.2023.10140746" @default.
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