Machine Learning for Alzheimer's Disease Detection and Categorization in Brain Images
Keywords:
Alzheimer’s disease, Magnetic Resonance Imaging (MRI), Mild Cognitive Impairment (MCI), skull strippingAbstract
Alzheimer's disease (AD) is a devastating form of dementia characterised by advanced symptoms in affected individuals' later years. Significant intellectual deficiencies, memory loss, and other cognitive impairments characterise the lives of Alzheimer's patients. Diagnosing Alzheimer's disease may be difficult and time-consuming due to the multitude of mental and physical tests neurologists often use. MCI, or mild cognitive impairment, is a kind of dementia that occurs in the early stages of Alzheimer's disease. The last stage of MCI is called late-MCI, and it is sometimes mistaken for the first stages of Alzheimer's disease (EAD). Correctly classifying EAD is also crucial for preventing or delaying the start of AD. The most fundamental modification in terms of AD's physical presentation is the degeneration of brain cells. Critical biomarkers related with the illness may be uncovered by careful analysis of brain images. The use of magnetic resonance imaging, commonly referred to as an MRI, is a common diagnostic tool used in the medical imaging area during clinical investigations. A large quantity of MRI data was collected from a number of publicly available sources in order to conduct this investigation. All the photos that were taken have had the "skull stripped" effect added to them. The skull and other non-brain pixels carry very little information, hence this is necessary
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