Alzheimer classification using Deep Learning technique

Abstract

A person with Alzheimer's disease may ultimately find it difficult to speak and react to their environment. A degenerative condition called Alzheimer's disease begins with minor memory loss. Recently, automated classification and early Alzheimer's disease diagnosis have drawn a lot of interest in deep learning. It has been demonstrated that convolutional neural networks (CNN) and other deep learning approaches outperform more conventional machine learning methods. Using mobility data and deep learning algorithms, this study seeks to determine a patient's stage of Alzheimer's disease. For classification research using machine learning, Feature selection, feature- based technique selection, dimensionality reduction and feature extraction are typically necessary four steps. These techniques may be time-consuming and call for specialist knowledge and several optimization stages. Among the recent studies, the Deep Learning approach has produced great results for picture classification. Medical imaging technology can be utilised to solve these issues, and neuroimaging methods like structural magnetic resonance imaging (SMRI) can be combined with deep learning algorithms to produce improved classification outcomes.