Survey on Early Detection of Alzhiemer’s Disease Using Capsule Neural Network

  • Sharunya Sharunya R
  • Vijayalakshmi Desai
  • Meenakshi Singh
  • Kusuma Mohanchandra
Keywords: Alzheimer's Disease, Content Based Image Retrieval, Magnetic Resonance Imaging

Abstract

Alzheimer's disease (AD) is an disorder which is irreversible of the brain related to memory loss, mostly found in the old and aged population. Alzheimer's dementia results from the degeneration or loss of brain cells. The brain-imaging technologies most often used to diagnose AD is Magnetic resonance imaging (MRI). MRI or structural magnetic resonance is a very popular and actual technique used to diagnose AD. An MRI uses magnets and powerful radio waves to create a complete view of your brain. To actually detect the presence of Alzheimer’s, the MRI should me studied carefullyImplementation of CBIR Content Based Image Retrival which is a revolutionary computer aided diagnosis technique will create new abilities in MRI Magnetic resonance imaging in related image retrieval and training for recognition of development of AD in early stages

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Author Biographies

Sharunya Sharunya R

Department of Information Science and Engineering. Dayananda Sagar Academy of Technology and Management, Bangalore, Karnataka, India.

Vijayalakshmi Desai

Department of Information Science and Engineering. Dayananda Sagar Academy of Technology and Management, Bangalore, Karnataka, India.

Meenakshi Singh

Department of Information Science and Engineering. Dayananda Sagar Academy of Technology and Management, Bangalore, Karnataka, India.

Kusuma Mohanchandra

Department of Information Science and Engineering. Dayananda Sagar Academy of Technology and Management, Bangalore, Karnataka, India.

This is an open access article, licensed under CC-BY-SA

Creative Commons License
Published
        Views : 526
2020-04-23
    Downloads : 368
How to Cite
[1]
S. Sharunya R, V. Desai, M. Singh, and K. Mohanchandra, “Survey on Early Detection of Alzhiemer’s Disease Using Capsule Neural Network”, International Journal of Artificial Intelligence, vol. 7, no. 1, pp. 7-12, Apr. 2020.
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Articles

References

Anonymous, “Alzheimer's disease facts and figures,” Alzheimer's & dementia: the journal of the Alzheimer's Association, vol. 11, no. 3, pp. 332, 2015.

D. Yao, V. D. Calhoun, Z. Fu, Y. Du, and J. Sui, “An ensemble learning system for a 4-way classification of Alzheimer’s disease and mild cognitive impairment,” Journal of neuroscience methods, vol. 302, pp. 75-81, 2018.

J. Liu, M. Li, W. Lan, F.X. Wu, Y. Pan, and J. Wang, “Classification of Alzheimer’s disease using whole brain hierarchical network,” IEEE/ACM transactions on computational biology and bioinformatics, vol. 15, no. 2, pp. 624 - 632, 2016.

N. Amoroso, D. Diacono, A. Fanizzi, M. La Rocca, A. Monaco, A. Lombardi, C. Guaragnella, R. Bellotti, S. Tangaro, and Alzheimer’s “Disease Neuroimaging Initiative, 2018 Deep learning reveals Alzheimer’s disease onset in MCI subjects: results from an international challenge,” Journal of neuroscience methods, vol. 302, pp. 3-9, 2018.

M., Liu, J. Zhang, P. T. Yap, and D., Shen, “View-aligned hypergraph learning for Alzheimer’s disease diagnosis with incomplete multi-modality data,” Medical image analysis, 36, pp.123 -134, 2017.

J. Zhang, Y. Gao, Y. Gao, B. C. Munsell, and D. Shen, “Detecting anatomical landmarks for fast Alzheimer’s disease diagnosis,” IEEE transactions on medical imaging, vol. 35, no. 12, pp. 2524-2533, 2016.

L. Nanni, C. Salvatore, A. Cerasa, and I. Castiglioni, “Combining multiple approaches for the early diagnosis of Alzheimer’s Disease,” Pattern Recognition Letters, vol. 84, pp. 259 - 266, 2016.

Y. Zhang, S. Wang, P. Phillips, Z. Dong, G. Ji, and J. Yang, “Detection of Alzheimer's disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA-KSVM trained by PSOTVAC,” Biomedical Signal Processing and Control, vol. 21, pp. 58-73, 2015.

A. Aramendi, A. Weakley, M. Schmitter-Edgecombe, D. J. Cook, A. Aztiria Goenaga, A. Basarab, and M. Barrenechea Carrasco, “Smart home-based prediction of multi-domain symptoms related to Alzheimer’s disease,” IEEE Journal of Biomedical and Health Informatics, 2018.

R. K. Lama, J. Gwak, J. S. Park, and S.W. Lee, “Diagnosis of Alzheimer’s disease based on structural MRI images using a regularized extreme learning machine and PCA features,” Journal of healthcare engineering, 2017.

N. Zeng, H. Qiu, Z. Wang, W. Liu, H. Zhang, and Y. Li, “A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer’s disease,” Neurocomputing, vol. 320, pp.195-202, 2018.

C. Laske, H. R. Sohrabi, S. M. Frost, K. López-de-Ipiña, P. Garrard, M. Buscema, J. Dauwels, S.R. Soekadar, S. Mueller, C. Linnemann, and S.A., “Bridenbaugh, Innovative diagnostic tools for early detection of Alzheimer's disease,” Alzheimer's & Dementia, vol. 11, no. 5, pp. 561-578, 2015.

G. A. Papakostas, A. Savio, M. Graña, and V. G. Kaburlasos, “A lattice computing approach to Alzheimer’s disease computer assisted diagnosis based on MRI data,” Neurocomputing”, vol. 150, pp. 37-42, 2015.

K. R. Kruthika, H. D. Maheshappa, and Alzheimer's “Disease Neuroimaging Initiative: CBIR system using Capsule Networks and 3D CNN for Alzheimer's disease diagnosis,” Informatics in Medicine Unlocked, vol. 14, pp. 59-68, 2019.

R. Ju, C. Hu, P. Zhou, and Q. Li, “Early diagnosis of Alzheimer's disease based on resting-state brain networks and deep learning,” IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), vol. 16, no. 1, pp. 244-257, 2019.

L. Yue, X. Gong, K. Chen, M. Mao, J. Li, A. K. Nandi, and M. Li, “Auto-Detection of Alzheimer's Disease Using Deep Convolutional Neural Networks,” in 2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), IEEE, pp. 228-234, July 2018.