지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.9 No.4
- 발행연도
- 2020.8
- 수록면
- 266 - 273 (8page)
- DOI
- 10.5573/IEIESPC.2020.9.4.266
이용수
초록· 키워드
오류제보하기
Breast cancer is the second most common cause of mortality in women. In the last decade, the rate of new diagnoses has increased significantly. Meanwhile, according to the World Health Organization, breast cancer can be treated effectively if it is detected in the early stages. Computer automated diagnosis systems have greatly helped researchers to learn the evolution and causes of breast cancer, as well as to monitor and detect the disease among women. However, there is a need for modern and accurate models to reduce the risk of breast cancer as well as for further improvements in diagnostic performance. In this paper, a deep belief network (DBN) model is proposed to achieve high accuracy in breast cancer classification. A particle swarm optimization algorithm is employed and integrated into the DBN to optimize the model’s parameters. The model is tested on two popular breast cancer datasets. Moreover, analysis of the datasets and improvements in data quality using data mining are shown, and an effective feature-selection method for a high-dimensional feature space is discussed. In experiments, the proposed method showed robust performance with high classification accuracy, matching, and even outperforming existing state-of-the-art algorithms.
목차
Abstract
1. Introduction
2. Related Work
3. The Proposed Model
4. Experiments
5. Conclusion
References
참고문헌 (21)
참고문헌 신청
2020 / World cancer research fund
Swe Swe Aung / 2018 / Noise-tolerance Investigation into Dual-kNN Pattern Classification / IEIE Transactions on Smart Processing & Computing 7(3):221~231
Dheeba, J. / 2014 / Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach / Journal of biomedical informatics
Abdel-Basset, M. / 2019 / A novel group decision making model based on neutrosophic sets for heart disease diagnosis / Multimedia Tools and Applications 1(26)
Kasivisvanathan, V / 2018 / MRI-targeted or standard biopsy for prostate-cancer diagnosis / New England Journal of Medicine 378(19):1767~1777
Swe Swe Aung / 2018 / Noise-tolerance Investigation into Dual-kNN Pattern Classification / IEIE Transactions on Smart Processing & Computing 7(3):221~231
Dheeba, J. / 2014 / Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach / Journal of biomedical informatics
Abdel-Basset, M. / 2019 / A novel group decision making model based on neutrosophic sets for heart disease diagnosis / Multimedia Tools and Applications 1(26)
Kasivisvanathan, V / 2018 / MRI-targeted or standard biopsy for prostate-cancer diagnosis / New England Journal of Medicine 378(19):1767~1777
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UCI(KEPA) : I410-ECN-0101-2020-569-001078711