Alzheimer’s Disease Detection and Classification: An Ensemble Machine Learning Paradigm

In industrialized nations, Alzheimer's disease (AD) ranks among the top ten causes of death. Research, including the use of computer-aided algorithms, has yielded outstanding results. Recently, deep models have gained traction, particularly for image processing tasks. There has been a dramatic increase in the number of publications published on the topic of AD detection using deep learning since 2017.Deep models have been shown to improve AD detection accuracy compared to more traditional forms of machine learning. Classifying instances of AD needs a highly discriminative feature representation to discriminate between otherwise similar brain patterns, making AD diagnosis challenging despite recent progress. This research detects the distinct forms of AD with the use of machine learning techniques. However, there are still certain hurdles to overcome, especially regarding dataset availability and training processes, notwithstanding deep learning's impressive performance in detecting AD.