Picture this: Alzheimer’s disease is like a mischievous maze that steals memories and jumbles thoughts. It’s an irreversible villain that brings great challenges and hardships to both patients and society. But fear not! Scientists are harnessing the power of artificial intelligence (AI) to combat this foe. In this exciting review, we delve into the world of machine learning and neuroimaging techniques to detect Alzheimer’s disease with precision and speed. We explore popular methods like support vector machine (SVM), random forest (RF), convolutional neural network (CNN), autoencoder, deep learning, and transformer. Get ready to meet powerful feature extractors and cutting-edge convolutional neural networks that can decode Alzheimer’s mysteries lurking in brain scans! But wait, there are obstacles to overcome – class imbalance and data leakage. Don’t worry, we discuss clever strategies like pre-processing techniques for smoother analysis and offer insights into choosing the best machine learning methods. So, put on your science hat, dive into the research, and together let’s unravel the secrets of Alzheimer’s with the help of AI!
Alzheimer’s disease (AD) is a neurodegenerative disorder that causes memory degradation and cognitive function impairment in elderly people. The irreversible and devastating cognitive decline brings large burdens on patients and society. So far, there is no effective treatment that can cure AD, but the process of early-stage AD can slow down. Early and accurate detection is critical for treatment. In recent years, deep-learning-based approaches have achieved great success in Alzheimer’s disease diagnosis. The main objective of this paper is to review some popular conventional machine learning methods used for the classification and prediction of AD using Magnetic Resonance Imaging (MRI). The methods reviewed in this paper include support vector machine (SVM), random forest (RF), convolutional neural network (CNN), autoencoder, deep learning, and transformer. This paper also reviews pervasively used feature extractors and different types of input forms of convolutional neural network. At last, this review discusses challenges such as class imbalance and data leakage. It also discusses the trade-offs and suggestions about pre-processing techniques, deep learning, conventional machine learning methods, new techniques, and input type selection.
Dr. David Lowemann, M.Sc, Ph.D., is a co-founder of the Institute for the Future of Human Potential, where he leads the charge in pioneering Self-Enhancement Science for the Success of Society. With a keen interest in exploring the untapped potential of the human mind, Dr. Lowemann has dedicated his career to pushing the boundaries of human capabilities and understanding.
Armed with a Master of Science degree and a Ph.D. in his field, Dr. Lowemann has consistently been at the forefront of research and innovation, delving into ways to optimize human performance, cognition, and overall well-being. His work at the Institute revolves around a profound commitment to harnessing cutting-edge science and technology to help individuals lead more fulfilling and intelligent lives.
Dr. Lowemann’s influence extends to the educational platform BetterSmarter.me, where he shares his insights, findings, and personal development strategies with a broader audience. His ongoing mission is shaping the way we perceive and leverage the vast capacities of the human mind, offering invaluable contributions to society’s overall success and collective well-being.