A Survey on Machine Learning and Deep Learning Techniques for Alzheimer’s Disease Prediction

Alzheimer’s disease (AD) is a brain condition that cannot be cured that gradually impairs cognitive function, symptoms can be greatly reduced with early identification. Nearly 20 years before AD is diagnosed, brain cell deterioration, synaptic dysfunction, and pathological alterations begin to manifest. A thorough examination of particular brain disorders’ tissues is necessary in order to precisely identify the condition using segmented magnetic resonance imaging (MRI). The literature review study investigates the use of MRI scans and deep learning algorithms for the identification of Alzheimer’s disease. Eight distinct research papers were examines, each employing various classification algorithms including pre-trained Convolutional Neural Networks. These research concentrated on categorising Alzheimer’s disease into several phases. The commonly utilized datasets in these investigations were ADNI and OASIS. The report provides an overview of the methodologies employed, emphasizing the diverse approaches taken in utilizing classification algorithms and CNN’s for Alzheimers disease stage classification based on MRI images.