Multiclass Diagnosis of Alzheimer’s Disease Analysis Using Machine Learning and Deep Learning Techniques

Author:

Begum Afiya Parveen1,Selvaraj Prabha1

Affiliation:

1. School of Computer Science and Engineering, VIT-AP University, Andhra Pradesh, India

Abstract

Alzheimer’s disease (AD) is a popular neurological disorder affecting a critical part of the world’s population. Its early diagnosis is extremely imperative for enhancing the quality of patients’ lives. Recently, improved technologies like image processing, artificial intelligence involving machine learning, deep learning, and transfer learning have been introduced for detecting AD. This review describes the contribution of image processing, feature extraction, optimization, and classification approach in AD recognition. It deeply investigates different methods adopted for multiclass diagnosis of AD. The paper further presents a brief comparison of existing AD studies in terms of techniques adopted, performance measures, classification accuracy, publication year, and datasets. It then summarizes the important technical barriers in reviewed works. This paper allows the readers to gain profound knowledge regarding AD diagnosis for promoting extensive research in this field.

Publisher

World Scientific Pub Co Pte Ltd

Subject

Computer Graphics and Computer-Aided Design,Computer Science Applications,Computer Vision and Pattern Recognition

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