Proceedings of the 6th International Conference on Intelligent Computing (ICIC-6 2023)

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Prediction and Feature Extraction Techniques used for Classification of Alzheimer’s Disease in its Early Stage using MRI: A Review

Authors
G. Merlin1, *, V. Pattabiraman2
1Vellore Institute of Technology, Vandalur Kelambakkam road, Tamil Nadu, 600127, Chennai, India
2Vellore Institute of Technology, Vandalur Kelambakkam road, Chennai, Tamil Nadu, India, 600127
*Corresponding author. Email: merlin.g2021@vitstudent.ac.in
Corresponding Author
G. Merlin
Available Online 17 October 2023.
DOI
10.2991/978-94-6463-250-7_18How to use a DOI?
Keywords
Neurological disorder; early diagnosis; Alzheimer’s disease; anatomy of brain; machine learning; deep learning
Abstract

Most people worldwide are affected by an acute neurological disorder called Alzheimer’s disease. Despite the reality that there is no treatment, it can be tapered off if it is detected early enough. 6 million individuals in the United States have Alzheimer's disease, and more than 120,000 people have died as a result of it. This has been designated as the sixth leading cause of death. Early diagnosis of AD can be useful to improve the quality of living of AD patients and will be helpful for their caretakers also. The anatomy of brain is intricate in structure packed with more information which makes it more burdensome to extract the features. This study outlines the machine learning and deep learning techniques utilized in the prediction and categorization of Alzheimer's disease, with an emphasis on the most recent approaches in feature extraction based on texture, voxel, wavelet and graph.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 6th International Conference on Intelligent Computing (ICIC-6 2023)
Series
Advances in Computer Science Research
Publication Date
17 October 2023
ISBN
10.2991/978-94-6463-250-7_18
ISSN
2352-538X
DOI
10.2991/978-94-6463-250-7_18How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - G. Merlin
AU  - V. Pattabiraman
PY  - 2023
DA  - 2023/10/17
TI  - Prediction and Feature Extraction Techniques used for Classification of Alzheimer’s Disease in its Early Stage using MRI: A Review
BT  - Proceedings of the 6th International Conference on Intelligent Computing (ICIC-6 2023)
PB  - Atlantis Press
SP  - 94
EP  - 100
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-250-7_18
DO  - 10.2991/978-94-6463-250-7_18
ID  - Merlin2023
ER  -