Machine and deep learning approaches for alzheimer disease detection using magnetic resonance images: An updated review

Abstract

The most frequent chronic illness affecting the elderly and one with a high incidence rate is Alzheimer's disease (AD). Deep Learning (DL) and Machine Learning (ML) techniques has significant success and gained popularity in medical imaging. It has emerged as the method of choice for examining medical images and has drawn considerable interest in identification of AD. The review paper mainly focus on image pre-processing which contributes on noise removal, illumination and intensity correction in Magnetic Resonance (MR) images, segmentation methods which helps in extracting the region of interest for AD detection, feature extraction which are considered as the inputs for classification and various machine and deep learning algorithms are analysed for detecting AD. The survey focus on papers related with academic year 2013 to 2023. The maximum number of paper considered for the review falls from 2019 with 13 papers, 2021 with 27, 2022 with 18 and 2023 with 22 papers and other papers are related with dataset reference and papers published before 2019. From the survey, deep learning techniques are more robust in detecting AD.