Machine learning evaluation demonstrates accurate four-stage Alzheimer classification in brain MRI datasets, suggesting viable automated clinical screening.
Alzheimer Disease commonly refers to a neurodegenerative brain disorder that leads to progressive impairment of cognitive abilities. Alzheimer constitutes 60-70% of dementia worldwide and holds the seventh position in the leading causes of death globally. Detecting Alzheimer's early is vital for timely, targeted interventions in the prodromal stage, optimizing the opportunity to mitigate cognitive decline and improve patient outcomes. Deep diving into the world of ML/AI aims to speed up early detection and mitigation of AD. This paper introduces a CNN model for Alzheimer stage detection on the OASIS dataset with 80,000 brain MRI images. It is accurately categorized into four stages (98% accuracy) and employs a weighted random sampler to balance the dataset during training, enhancing the model's performance.
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Sinha et al. (2024) studied this question.
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