An early-stage Alzheimer's disease detection using various imaging modalities and techniques – A mini-review

Authors

  • T. Deenadayalan Vellore Institue of Technology
  • S.P. Shantharajah Vellore Institue of Technology

DOI:

https://doi.org/10.62110/sciencein.jist.2024.v12.803

Keywords:

Machine Learning, Medical Image Analysis, Predictive analysis, Alzheimer's disease detection

Abstract

Alzheimer's is a disease that affects the brain parts and leads the cells of the brain to die. It is a permanent disorder that causes danger in memory and loss the responsiveness related to the environment. The brain network plays a significant part in the identification of (Alzheimer's Disease) AD and (Mild Cognitive Impairment) MCI disorders. Since the Alzheimer's Association cautioned that Alzheimer’s disease will affect 1 in 85 people by 2050, it is highly essential to have a role play to get a faster diagnosis and a prognosis. The biomarker used to diagnose the disease for distinguishing across various dementia causes needs early detection. Machine learning (ML) uses a variety of techniques to allow (Normal Controls) NCs to benefit from high dimensional data sets. This paper presents a study in early-stage identification or classification of AD using different transferred ML techniques with different modalities and their critical assessment and analysis.

URN: NBN: sciencein.jist.2024.v12.803

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Author Biographies

  • T. Deenadayalan, Vellore Institue of Technology

    School of Computer Science and Engineering and Information Technology

  • S.P. Shantharajah, Vellore Institue of Technology

    School of Computer Science Engineering and Information Systems

Alzeihmer's disease detection using machine learning

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Published

2024-02-05

Issue

Section

Computer Sciences and Mathematics

URN

How to Cite

An early-stage Alzheimer’s disease detection using various imaging modalities and techniques – A mini-review. (2024). Journal of Integrated Science and Technology, 12(5), 803. https://doi.org/10.62110/sciencein.jist.2024.v12.803

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