Paper Contents
Abstract
Alzheimer's disease is a progressive neurodegenerative disease that is predominant in the classic aging population, affecting countless individuals worldwide. While early diagnosis is valuable for timely intervention, conventional methods of diagnosis do not always provide the necessary level of detail needed for the inception of the disease. The advent of the new generation of machine learning (ML) and deep learning (DL) algorithms brings with it possibilities to enhance diagnostic accuracy through automated, scalable, and efficient detection methodologies.The literature survey thus attempts to cover the helpings of the latest machine learning mechanisms aimed at diagnosing AD including SVM, RF, CNNs, transfer learning, and hybrid models. While we will each analyze the different approaches, also presented will be comparisons of the effectiveness, pros, and cons of each, thereby offering some future insights toward the clinical practicality of these models.
Copyright
Copyright © 2024 Lokesh Agrawal. This is an open access article distributed under the Creative Commons Attribution License.
