Detection and Classification of MRI Images using Multistage Classifier for Early Prediction of Alzheimer’s Disease

Abstract

The term “neuro imaging” describes a set of procedures used for studying, diagnosing, and treating neurological disorders as well as for basic scientific research into the human brain. In order for medical professionals to make early diagnoses for their patients, they must investigate the signs of various neuro imaging kinds; among the most important elements in this process is the problem of neuro image classification. No effective treatment has been found for Alzheimer's disease (AD), a degenerative brain ailment that has no known cure. It is not possible to halt the progression of the disease once it has begun. But there are medications that can slow it down. Nevertheless, with the use of sophisticated prediction, the disease-influencing protein functions can be reduced. Because of shared neural networks and pixel strength, Alzheimer's disease diagnosis in the elderly is discretely challenging and calls for the portrayal of a discriminating element separately. Several writers attempted to apply machine learning to make early disease diagnoses, but they were unable to get reliable classification accuracy. To that end, we set out to develop a model that would combine deep neural networks with multistage classifiers in the hopes that it would be able to efficiently and effectively extract characteristics from input data. Better and more effective detection and classification of Alzheimer's disease was achieved in this study by employing a multistage classifier that made use of deep learning techniques. Using a benchmark database supplied by the Alzheimer's Neuro Imaging Institute, the suggested strategy achieves better outcomes than individual techniques.