Malignant tumor detection using linear support vector machine in breast cancer based on new optimization algorithms

Naeemabadi, M ; Sharif University of Technology | 2012

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  1. Type of Document: Article
  2. DOI: 10.1109/MSNA.2012.6324520
  3. Publisher: 2012
  4. Abstract:
  5. Breast cancer is one of the most common fatal diseases in women. Early detection of malignant breast cancer could be a great help in treating this cancer. Many studies have been performed in order to detect the malignant of cancer tumor till now. It has been tried to contribute more in accurate diagnosis of breast cancer by Support Vector Machine, in this paper. LS and SMO methods have been utilized instead of conventional learning method of QP in SVM in this probe. The feasibility of 100 percent in sensitivity for LS-SVM, and 100 percent in specificity for SMO-SVM has been achieved in this assay by the proposed method, which this percentage has not been achieved so far in the previous studies. The highest value among the previous studies has been presented by the obtained accuracy in LS-SVM method
  6. Keywords:
  7. Least Square SVM ; Malignant Tumor ; Sequential Minimal Optimization SVM ; Least Square ; Linear Support Vector Machines ; Malignant tumors ; Sequential minimal optimization ; Algorithms ; Diagnosis ; Diseases ; Measurements ; Optimization ; Sensor networks ; Tumors ; Support vector machines
  8. Source: Proceedings - 2012 International Symposium on Instrumentation and Measurement, Sensor Network and Automation, IMSNA 2012 ; Volume 1 , 2012 , Pages 80-84 ; 9781467324670 (ISBN)
  9. URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6324520