Machine Learning Based Alzheimer’s Disease Detection in MRI Images

Authors

  • Anil N S Dept. Electronics and communication, East West Institute of Technology, Bangalore, India
  • Bharghavi M Dept. Electronics and communication, East West Institute of Technology, Bangalore, India
  • Chandrika K J Dept. Electronics and communication, East West Institute of Technology, Bangalore, India,
  • Divyashree L B Dept. Electronics and communication, East West Institute of Technology, Bangalore, India
  • Hema K N Dept. Electronics and communication, East West Institute of Technology, Bangalore, India

DOI:

https://doi.org/10.5281/zenodo.5762177

Keywords:

MRI, Alzheimer’s Disease, CAD, Survey, Computer Vision, Dementia, Machine Learning

Abstract

Magnetic Resonance Image is a medical imaging technique which can be used to diagnose problems associated with brain. The advantage of this technique is that it is completed contactless – no need to do any surgery to know what’s wrong with the brain. This paper shows different ways through which Dementia can be diagnosed, all by using MRI images and computer vision. Also in this paper, a machine learning based method is proposed to identify Alzheimer’s disease.

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References

Aruchamy, Srinivasan, Ravi Kant Kumar, Partha Bhattacharjee, and Goutam Sanyal. "Automated skull stripping in brain MR images." In Computing for Sustainable Global Development (INDIACom), 2016 3rd International Conference on, pp. 2043-2047. IEEE, 2016.

Srinivasan A ,P Battacharjee ,Ananda Prasad I ,Goutam Sanyal . Brain MRI analysis using Discrete wavelet transform with fractal feature analysis.IEEE conference record #42487.

Jian Li, Qian Du, and Caixin Sun. 2009. An improved box-counting method for image fractal dimension estimation. Pattern Recogn. 42, 11 (November 2009), 2460–2469.

Olfa Ben Ahmed, Jenny Benois-Pineau, Michèle Allard, Chokri Ben Amar, and Gwénaëlle Catheline. 2015. Classification of Alzheimer’s disease subjects from MRI using hippocampal visual features. Multimedia Tools Appl. 74, 4 (February 2015), 1249–1266.

A. Shams-Baboli and M. Ezoji, "A Zernike moment based method for classification of Alzheimer's disease from structural MRI," 2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA), Shahrekord, 2017, pp. 38-43.

M. Wehenkel, C. Bastin, C. Phillips and P. Geurts, "Tree ensemble methods and parcelling to identify brain areas related to Alzheimer’s disease," 2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI), Toronto, ON, 2017, pp. 1-4.

S. Korolev, A. Safiullin, M. Belyaev and Y. Dodonova, "Residual and plain convolutional neural networks for 3D brain MRI classification," 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, VIC, 2017, pp. 835-838.

Jack CR, Jr., Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D, et al. The Alzheimer’s Disease Neuroimaging Initiative (ADNI): MRI methods. J Magn Reson Imaging. 2008;27(4):685–691. doi:10.1002/jmri.21049

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Published

2021-12-05

How to Cite

[1]
A. N. S, B. M, C. K. J, D. L. B, and H. K. N, “Machine Learning Based Alzheimer’s Disease Detection in MRI Images”, pices, vol. 5, no. 8, pp. 79-82, Dec. 2021.

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