Machine Learning Approaches to Neuroimaging Data for Early Alzheimer's Disease Detection with Emphasis on Convolutional Neural Networks' Superior Performance

For effective treatment and better results, it is essential to detect Alzheimer's disease (AD) at its earliest stages. Understanding the anatomical and functional changes in the brain associated with AD has become increasingly dependent on neuroimaging data. This work includes a systematic review of previous research on the use of Machine Learning (ML) methods on neuroimaging data for the diagnosis of AD in its earliest stages. We thoroughly compared and contrasted deep learning architectures with more conventional ML methods. In our approach, we first preprocessed the neuroimaging data to improve feature extraction, and then we trained the models on a carefully selected dataset that included data from people with AD and those without the disease. According to the findings, deep learning models, in particular CNNs, performed better than conventional ML methods, successfully differentiating early-stage AD from normal ageing with an accuracy of 94.7%. This research highlights the importance of ML in changing the way AD is diagnosed, providing hope for improved clinical decision-making and earlier intervention.