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کاربرد نوع شرط:
- جایگاه : پژوهشی
- مجله: Asian Pacific Journal of Cancer Prevention
- نوع مقاله: Journal Article
- کلمات کلیدی: Breast cancer,Mammogram,discriminant Analysis,cuckoo-search
- چکیده:
- چکیده انگلیسی: Objective: Breast cancer is the most common invasive severity which leads to the second primary cause of death
among women. The objective of this paper is to propose a computer-aided approach for the breast cancer classification
from the digital mammograms. Methods: Designing an effective classification approach will assist in resolving the
difficulties in analyzing digital mammograms. The proposed work utilized the Mammogram Image Analysis Society
(MIAS) database for the analysis of breast cancer. Five distinct wavelet families are used for extraction of features
from the mammograms of MIAS database. These extracted features are statistical in nature and served as input to the
Linear Discriminant Analysis (LDA) and Cuckoo-Search Algorithm (CSA) classifiers. Results: Error rate, Sensitivity,
Specificity and Accuracy are the performance measures used and the obtained results clearly state that the CSA used
as a classifier affords an accuracy of 97.5% while compared with the LDA classifier. Conclusion: The results of
comparative performance analysis show that the CSA classifier outperforms the performance of LDA in terms of breast
cancer classification.- انتشار مقاله: 08-11-1397
- نویسندگان: Sannasi Chakravarthy S R,Harikumar Rajaguru
- مشاهده