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Research Article
An Alzheimer’s Disease Image Feature Extraction and Different Classification in Machine Learning Algorithm
Prabakaran N1
Dr. Prabhakaran Paulraj2
1Department of Computer Science and Engineering, Sai Rejeswari Institute of Technology, Proddatur, Andhra Pradesh, India. 2Department of Computer Science and Engineering, St. Joseph College of Engineering and Technology, Chennai, Tamil Nadu, India.
Published Online: May-August 2024
Pages: 218-224
Cite this article
No DOIReferences
1. D. Deepa, Dr.Anitha Karthi, An Image Feature extraction technique Alzheimer’s disease using inductive learning, Indian Journal of
Computer Science and Engineering (IJCSE), Vol. 13 No. 1 Jan-Feb 2022. pp: 278-287
2. Oh, K., Chung, YC., Kim, K.W. et al. Classification and Visualization of Alzheimer’s Disease using Volumetric Convolutional Neural Network
and Transfer Learning. Sci Rep 9, 18150 (2019). https://doi.org/10.1038/s41598-019-54548-6
3. Grundman, M., Petersen, R. C. & Ferris, S. H. Mild cognitive impairment can be distinguished from alzheimer disease and normal aging for
clinical trials. Archives of Neurology. 61, 59–66 (2014).
4. Davatzikos, C., Fan, Y., Wu, D. X. & Shen, S. M. Detection of prodromal Alzheimer’s disease via pattern classifcation of magnetic resonance
imaging. Neurobiology of Aging. 29, 514–523 (2018).
5. Bar, Y., Diamant, I., Wolf, L. & Greenspan, H. Deep learning with non-medical training used for chest pathology identifcation. In: SPIE
Medical Imaging, https://doi.org/10.1117/12.2083124 (2015).6. Abrol, a et al. Deep Residual Learning for Neuroimaging: An application to Predict Progression to Alzheimer’s Disease.
bioRxiv,https://doi.org/10.1101/470252 (2018).
7. Fedorov, A. et al. Prediction of Progression to Alzheimer’s disease with Deep InfoMax. arXiv:1904.10931 (2019).22. Rieke, J., Fabian, E.,
Weygandt, M., Haynes, J. D. & Ritter, K. Visualizing Convolutional Networks for MRI-Based Diagnosis of Alzheimer’s Disease. MICCAI.
24–31 (2018).
8. Yang, C., Rangarajan, A. & Rnaka, S. Visual Explanations from Deep 3D Convolutional Neural Networks for Alzheimer’s Disease
Classifcation. In: AMIA (2018).
9. Korolev, S., Safiullin, A., Belyaev, M. & Dodonova, Y. Residual and plain convolutional neural networks for 3D brain MRI classification. In
ISBI, https://doi.org/10.1109/ISBI.2017.7950647 (2017).
10. Li Q, Yang MQ. 2021. Comparison of machine learning approaches for enhancing Alzheimer’s disease
classification. PeerJ 9:e10549 https://doi.org/10.7717/peerj.10549
11. Islam, J., and Zhang, Y. (2018). Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural
networks. Brain Inform. 5:2.
Computer Science and Engineering (IJCSE), Vol. 13 No. 1 Jan-Feb 2022. pp: 278-287
2. Oh, K., Chung, YC., Kim, K.W. et al. Classification and Visualization of Alzheimer’s Disease using Volumetric Convolutional Neural Network
and Transfer Learning. Sci Rep 9, 18150 (2019). https://doi.org/10.1038/s41598-019-54548-6
3. Grundman, M., Petersen, R. C. & Ferris, S. H. Mild cognitive impairment can be distinguished from alzheimer disease and normal aging for
clinical trials. Archives of Neurology. 61, 59–66 (2014).
4. Davatzikos, C., Fan, Y., Wu, D. X. & Shen, S. M. Detection of prodromal Alzheimer’s disease via pattern classifcation of magnetic resonance
imaging. Neurobiology of Aging. 29, 514–523 (2018).
5. Bar, Y., Diamant, I., Wolf, L. & Greenspan, H. Deep learning with non-medical training used for chest pathology identifcation. In: SPIE
Medical Imaging, https://doi.org/10.1117/12.2083124 (2015).6. Abrol, a et al. Deep Residual Learning for Neuroimaging: An application to Predict Progression to Alzheimer’s Disease.
bioRxiv,https://doi.org/10.1101/470252 (2018).
7. Fedorov, A. et al. Prediction of Progression to Alzheimer’s disease with Deep InfoMax. arXiv:1904.10931 (2019).22. Rieke, J., Fabian, E.,
Weygandt, M., Haynes, J. D. & Ritter, K. Visualizing Convolutional Networks for MRI-Based Diagnosis of Alzheimer’s Disease. MICCAI.
24–31 (2018).
8. Yang, C., Rangarajan, A. & Rnaka, S. Visual Explanations from Deep 3D Convolutional Neural Networks for Alzheimer’s Disease
Classifcation. In: AMIA (2018).
9. Korolev, S., Safiullin, A., Belyaev, M. & Dodonova, Y. Residual and plain convolutional neural networks for 3D brain MRI classification. In
ISBI, https://doi.org/10.1109/ISBI.2017.7950647 (2017).
10. Li Q, Yang MQ. 2021. Comparison of machine learning approaches for enhancing Alzheimer’s disease
classification. PeerJ 9:e10549 https://doi.org/10.7717/peerj.10549
11. Islam, J., and Zhang, Y. (2018). Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural
networks. Brain Inform. 5:2.
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