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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.16 No.1
- 발행연도
- 2022.3
- 수록면
- 1 - 13 (13page)
- DOI
- 10.5626/JCSE.2022.16.1.1
이용수
초록· 키워드
오류제보하기
Detection of Alzheimer’s disease (AD) is one of the most potent and daunting activities in the processing of medical imagery. The survey of recent AD detection techniques in the last 10 years is described in this paper. The AD detection process involves various steps, namely preprocessing, feature extraction, feature selection, dimensionality reduction, segmentation and classification. In this study, we reviewed the latest findings and possible patterns as well as their main contributions. Different types of AD detection techniques are also discussed. Based on the applied algorithms and methods, and the evaluated databases (e.g., ADNI and OASIS), the performances of the most relevant AD detection techniques are compared and discussed.
목차
Abstract
I. INTRODUCTION
II. MEDICAL IMAGING TECHNOLOGIES
III. AD DETECTION METHODS
IV. DATA COMPARISON
V. DISCUSSION
VI. CONCLUSION
REFERENCES
참고문헌 (72)
참고문헌 신청
A. Farzan, S. Mashohor, and R. Mahmud, “Boosting diagnosis accuracy of Alzheimer's disease using high dimensional recognition of longitudinal brain atrophy patterns,” Behavioural Brain Research, vol. 290, pp. 124-130, 2015.
D. Sarwinda and A. Bustamam, “Detection of Alzheimer's disease using advanced local binary pattern from hippocampus and whole brain of MR images,” in Proceedings of 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, Canada, 2016, pp. 5051-5056.
H. Mohsen, E. S. A. El-Dahshan, E. S. M. El-Horbaty, and A. B. M. Salem, “Classification of brain MRI for Alzheimer's disease based on linear discriminate analysis,” Egyptian Computer Science Journal, vol. 41, no. 3, pp. 44-52, 2017.
L. J. Herrera, I. Rojas, H. Pomares, A. Guillen, O. Valenzuela, and O. Banos, “Classification of MRI images for Alzheimer's disease detection,” in Proceedings of 2013 International Conference on Social Computing, Alexandria, VA, 2013, pp. 846-851.
D. Jha, J. I. Kim, and G. R. Kwon, “Diagnosis of Alzheimer’s disease using dual-tree complex wavelet transform, PCA, and feed-forward neural network,” Journal of Healthcare Engineering, vol. 2017, article no. 9060124, 2017. https://doi.org/10.1155/2017/9060124
D. Sarwinda and A. Bustamam, “Detection of Alzheimer's disease using advanced local binary pattern from hippocampus and whole brain of MR images,” in Proceedings of 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, Canada, 2016, pp. 5051-5056.
H. Mohsen, E. S. A. El-Dahshan, E. S. M. El-Horbaty, and A. B. M. Salem, “Classification of brain MRI for Alzheimer's disease based on linear discriminate analysis,” Egyptian Computer Science Journal, vol. 41, no. 3, pp. 44-52, 2017.
L. J. Herrera, I. Rojas, H. Pomares, A. Guillen, O. Valenzuela, and O. Banos, “Classification of MRI images for Alzheimer's disease detection,” in Proceedings of 2013 International Conference on Social Computing, Alexandria, VA, 2013, pp. 846-851.
D. Jha, J. I. Kim, and G. R. Kwon, “Diagnosis of Alzheimer’s disease using dual-tree complex wavelet transform, PCA, and feed-forward neural network,” Journal of Healthcare Engineering, vol. 2017, article no. 9060124, 2017. https://doi.org/10.1155/2017/9060124
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