상세정보
학술대회
An Optimized Support Vector Machine Classifier to Extract Abnormal Features from Breast Microwave Tomography Data
- 발행일
- 201410
- 출처
- Research in Adaptive and Convergent Systems (RACS) 2014, pp.111-115
- 협약과제
- 14MR1700, 전자파 이용 조기진단 고정밀 MT 시스템 개발, 전순익
- 초록
- Microwave Tomography (MT) as a new electronic healthcare system tries to measure dielectric properties of tissues inside the breast and helps early breast cancer detection. In this paper, we propose a new classifier to extract tumor information from Microwave Tomography raw data to determine whether the breast needs further diagnosis or not. The proposed method uses grid search algorithm to optimize support vector machine classifier. The results show that optimized SVM can improve measure of performances such as MCC, specificity and sensitivity. The new classifier can be a promising tool to provide preliminary decision support information to physicians for further diagnosis.
- KSP 제안 키워드
- Breast Cancer(BC), Breast cancer detection, Dielectric properties, Early breast cancer, Electronic healthcare, Healthcare Systems, Microwave tomography, Search Algorithm(GSA), Support VectorMachine(SVM), Support vector Machine Classifier, decision support