A Survey on Early Detection of Alzheimer’s using PET Scan

Authors

  • Jason Francis Fernandes Student, Department of Information Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore, India
  • B. Sampan Student, Department of Information Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore, India
  • Manoj Kumar Student, Department of Information Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore, India
  • T. R. Nithin Student, Department of Information Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore, India
  • M. Kusuma Professor, Department of Information Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore, India

Keywords:

Alzheimer’s, Data processing, Feature extraction, Machine Learning, PET

Abstract

Amyloid beta protein is usually the main precursor used to arrive at the conclusion if the subject has Alzheimer’s disease. Most of the classification techniques tend to classify the three stages namely Alzheimer's disease, cognitively normal and mild cognitive impairment. The other classification techniques tend to just give the output as to which the subject has Alzheimer’s disease. The objective of this paper is to assess the image processing techniques used, features extracted, image classification technique used and from where the dataset is downloaded.

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Published

02-04-2022

Issue

Vol. 5 No. 3 (2022)

Section

Articles

License

Copyright (c) 2022 Jason Francis Fernandes, B. Sampan, Manoj Kumar, T. R. Nithin, M. Kusuma

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
J. F. Fernandes, B. Sampan, M. Kumar, T. R. Nithin, and M. Kusuma, “A Survey on Early Detection of Alzheimer’s using PET Scan”, IJRESM, vol. 5, no. 3, pp. 155–157, Apr. 2022, Accessed: Apr. 23, 2024. [Online]. Available: https://journal.ijresm.com/index.php/ijresm/article/view/1885