A Review on Alzheimer's Disease Through Analysis of MRI Images Using Deep Learning Techniques

Abstract

The anatomical structure of the brain has been studied with the help of magnetic resonance imaging (MRI), which has been used to analyze numerous neurological diseases and define pathological areas. Early detection of Alzheimer's Disease (AD) patients is critical in order to implement preventative measures. Alzheimer's disease (AD) is the most common chronic disease in the elderly, with a high incidence rate. In recent years, deep learning has seen a lot of success in the medical image analysis. Brain diseases can be more accurately categorized using segmented MRI scans due to in-depth analyses of tissue architecture. Many, complex segmentation approaches have been presented for AD diagnosis. Since deep learning algorithms can yield effective results over a large data collection, they have received interest for use in segmenting the brain's structure and classifying AD. Consequently, the deep learning techniques are currently favored over machine learning techniques. We discuss how convolutional neural network concepts can be used to study brain anatomy in order to detect AD. New techniques, their results on open datasets, and the benefits of brain MRI segmentation for Alzheimer's disease categorization are discussed. In this article, the literature on Alzheimer's disease is briefly reviewed, and the possibility of Deep Learning to improve early diagnosis is discussed.