Automatic Machine Learning Classification of Alzheimer's Disease Based on Selected Slices from 3D Magnetic Resonance Imagining

Abdalla R. Gad; N. M. Hussein Hassan; Rania A. Abul Seoud; Tamer M. Nassef

doi:doi:10.11648/j.ijbse.20160406.11

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Automatic Machine Learning Classification of Alzheimer's Disease Based on Selected Slices from 3D Magnetic Resonance Imagining

Abdalla R. Gad, N. M. Hussein Hassan, Rania A. Abul Seoud, Tamer M. Nassef

Published in International Journal of Biomedical Science and Engineering (Volume 4, Issue 6)

Received: 31 October 2016 Accepted: 26 December 2016 Published: 15 February 2017

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Abstract

The most dominant form of dementia, memory loss, is Alzheimer's disease (AD). Imaging is important for monitoring, diagnosis, and education of Alzheimer's disease prediction. Automated classification of subjects could provide support for clinicians. This study examined two classification methods to separate among elderly persons with normal cognitive (NC), Alzheimer's disease (AD), and mild cognitive impairment (MCI) by using images from the magnetic resonance imaging (MRI). The dataset consists of 120 subjects separated into 40 ADs, 40 MCIs, and 40 NCs. The first technique was K-Nearest Neighbor (KNN) and the second technique was Support Vector Machine (SVM), firstly all the subjects were filtered and normalized, secondly twelve features were extracted. After feature selection, two techniques of classification were examined with Permutations and combinations for all features in order to select the best features which have the highest accuracy for identification the classes. The best average accuracy was 97.92% using SVM polynomial order three, and best all average accuracy was 95.833% using KNN with K=6, and K=7 for random selection of testing data with SVM and KNN. The results show a relatively high classification accuracy between the three clinical categories. In summary, the proposed automatic classification technique can be used as a noninvasive diagnostic tool for Alzheimer's disease, with the capability of defining early stages of the disease.

Published in	International Journal of Biomedical Science and Engineering (Volume 4, Issue 6)
DOI	10.11648/j.ijbse.20160406.11
Page(s)	50-54
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Alzheimer's Disease, Magnetic Resonance Imaging, Feature Extraction, Classification, Support Vector Machine, K-nearest Neighbor

References

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APA Style

Abdalla R. Gad, N. M. Hussein Hassan, Rania A. Abul Seoud, Tamer M. Nassef. (2017). Automatic Machine Learning Classification of Alzheimer's Disease Based on Selected Slices from 3D Magnetic Resonance Imagining. International Journal of Biomedical Science and Engineering, 4(6), 50-54. https://doi.org/10.11648/j.ijbse.20160406.11

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ACS Style

Abdalla R. Gad; N. M. Hussein Hassan; Rania A. Abul Seoud; Tamer M. Nassef. Automatic Machine Learning Classification of Alzheimer's Disease Based on Selected Slices from 3D Magnetic Resonance Imagining. Int. J. Biomed. Sci. Eng. 2017, 4(6), 50-54. doi: 10.11648/j.ijbse.20160406.11

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AMA Style

Abdalla R. Gad, N. M. Hussein Hassan, Rania A. Abul Seoud, Tamer M. Nassef. Automatic Machine Learning Classification of Alzheimer's Disease Based on Selected Slices from 3D Magnetic Resonance Imagining. Int J Biomed Sci Eng. 2017;4(6):50-54. doi: 10.11648/j.ijbse.20160406.11

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@article{10.11648/j.ijbse.20160406.11,
  author = {Abdalla R. Gad and N. M. Hussein Hassan and Rania A. Abul Seoud and Tamer M. Nassef},
  title = {Automatic Machine Learning Classification of Alzheimer's Disease Based on Selected Slices from 3D Magnetic Resonance Imagining},
  journal = {International Journal of Biomedical Science and Engineering},
  volume = {4},
  number = {6},
  pages = {50-54},
  doi = {10.11648/j.ijbse.20160406.11},
  url = {https://doi.org/10.11648/j.ijbse.20160406.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijbse.20160406.11},
  abstract = {The most dominant form of dementia, memory loss, is Alzheimer's disease (AD). Imaging is important for monitoring, diagnosis, and education of Alzheimer's disease prediction. Automated classification of subjects could provide support for clinicians. This study examined two classification methods to separate among elderly persons with normal cognitive (NC), Alzheimer's disease (AD), and mild cognitive impairment (MCI) by using images from the magnetic resonance imaging (MRI). The dataset consists of 120 subjects separated into 40 ADs, 40 MCIs, and 40 NCs. The first technique was K-Nearest Neighbor (KNN) and the second technique was Support Vector Machine (SVM), firstly all the subjects were filtered and normalized, secondly twelve features were extracted. After feature selection, two techniques of classification were examined with Permutations and combinations for all features in order to select the best features which have the highest accuracy for identification the classes. The best average accuracy was 97.92% using SVM polynomial order three, and best all average accuracy was 95.833% using KNN with K=6, and K=7 for random selection of testing data with SVM and KNN. The results show a relatively high classification accuracy between the three clinical categories. In summary, the proposed automatic classification technique can be used as a noninvasive diagnostic tool for Alzheimer's disease, with the capability of defining early stages of the disease.},
 year = {2017}
}

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TY - JOUR
T1 - Automatic Machine Learning Classification of Alzheimer's Disease Based on Selected Slices from 3D Magnetic Resonance Imagining
AU - Abdalla R. Gad
AU - N. M. Hussein Hassan
AU - Rania A. Abul Seoud
AU - Tamer M. Nassef
Y1 - 2017/02/15
PY - 2017
N1 - https://doi.org/10.11648/j.ijbse.20160406.11
DO - 10.11648/j.ijbse.20160406.11
T2 - International Journal of Biomedical Science and Engineering
JF - International Journal of Biomedical Science and Engineering
JO - International Journal of Biomedical Science and Engineering
SP - 50
EP - 54
PB - Science Publishing Group
SN - 2376-7235
UR - https://doi.org/10.11648/j.ijbse.20160406.11
AB - The most dominant form of dementia, memory loss, is Alzheimer's disease (AD). Imaging is important for monitoring, diagnosis, and education of Alzheimer's disease prediction. Automated classification of subjects could provide support for clinicians. This study examined two classification methods to separate among elderly persons with normal cognitive (NC), Alzheimer's disease (AD), and mild cognitive impairment (MCI) by using images from the magnetic resonance imaging (MRI). The dataset consists of 120 subjects separated into 40 ADs, 40 MCIs, and 40 NCs. The first technique was K-Nearest Neighbor (KNN) and the second technique was Support Vector Machine (SVM), firstly all the subjects were filtered and normalized, secondly twelve features were extracted. After feature selection, two techniques of classification were examined with Permutations and combinations for all features in order to select the best features which have the highest accuracy for identification the classes. The best average accuracy was 97.92% using SVM polynomial order three, and best all average accuracy was 95.833% using KNN with K=6, and K=7 for random selection of testing data with SVM and KNN. The results show a relatively high classification accuracy between the three clinical categories. In summary, the proposed automatic classification technique can be used as a noninvasive diagnostic tool for Alzheimer's disease, with the capability of defining early stages of the disease.
VL - 4
IS - 6
ER -

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Author Information

Abdalla R. Gad

Electronic and Communication Department, Faculty of Engineering, October High Institute for Engineering and Technology, Giza, Egypt
N. M. Hussein Hassan

Electronic and Communication Department, Faculty of Engineering, Fayoum University, Fayoum, Egypt
Rania A. Abul Seoud

Electronic and Communication Department, Faculty of Engineering, Fayoum University, Fayoum, Egypt
Tamer M. Nassef

Computer and Software Department, Faculty of Engineering, Misr University for Science and Technology, Giza, Egypt

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APA Style

Abdalla R. Gad, N. M. Hussein Hassan, Rania A. Abul Seoud, Tamer M. Nassef. (2017). Automatic Machine Learning Classification of Alzheimer's Disease Based on Selected Slices from 3D Magnetic Resonance Imagining. International Journal of Biomedical Science and Engineering, 4(6), 50-54. https://doi.org/10.11648/j.ijbse.20160406.11

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ACS Style

Abdalla R. Gad; N. M. Hussein Hassan; Rania A. Abul Seoud; Tamer M. Nassef. Automatic Machine Learning Classification of Alzheimer's Disease Based on Selected Slices from 3D Magnetic Resonance Imagining. Int. J. Biomed. Sci. Eng. 2017, 4(6), 50-54. doi: 10.11648/j.ijbse.20160406.11

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AMA Style

Abdalla R. Gad, N. M. Hussein Hassan, Rania A. Abul Seoud, Tamer M. Nassef. Automatic Machine Learning Classification of Alzheimer's Disease Based on Selected Slices from 3D Magnetic Resonance Imagining. Int J Biomed Sci Eng. 2017;4(6):50-54. doi: 10.11648/j.ijbse.20160406.11

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@article{10.11648/j.ijbse.20160406.11,
  author = {Abdalla R. Gad and N. M. Hussein Hassan and Rania A. Abul Seoud and Tamer M. Nassef},
  title = {Automatic Machine Learning Classification of Alzheimer's Disease Based on Selected Slices from 3D Magnetic Resonance Imagining},
  journal = {International Journal of Biomedical Science and Engineering},
  volume = {4},
  number = {6},
  pages = {50-54},
  doi = {10.11648/j.ijbse.20160406.11},
  url = {https://doi.org/10.11648/j.ijbse.20160406.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijbse.20160406.11},
  abstract = {The most dominant form of dementia, memory loss, is Alzheimer's disease (AD). Imaging is important for monitoring, diagnosis, and education of Alzheimer's disease prediction. Automated classification of subjects could provide support for clinicians. This study examined two classification methods to separate among elderly persons with normal cognitive (NC), Alzheimer's disease (AD), and mild cognitive impairment (MCI) by using images from the magnetic resonance imaging (MRI). The dataset consists of 120 subjects separated into 40 ADs, 40 MCIs, and 40 NCs. The first technique was K-Nearest Neighbor (KNN) and the second technique was Support Vector Machine (SVM), firstly all the subjects were filtered and normalized, secondly twelve features were extracted. After feature selection, two techniques of classification were examined with Permutations and combinations for all features in order to select the best features which have the highest accuracy for identification the classes. The best average accuracy was 97.92% using SVM polynomial order three, and best all average accuracy was 95.833% using KNN with K=6, and K=7 for random selection of testing data with SVM and KNN. The results show a relatively high classification accuracy between the three clinical categories. In summary, the proposed automatic classification technique can be used as a noninvasive diagnostic tool for Alzheimer's disease, with the capability of defining early stages of the disease.},
 year = {2017}
}

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