Title :
Malignant tumor detection using linear support vector machine in breast cancer based on new optimization algorithms
Author :
Naeemabadi, Mohammadreza ; Saleh, Mohamadreza Afshari ; Zabihi, Morteza ; Mohseni, Golamreza ; Chomachar, Nooshaz Amirahmadi
Author_Institution :
Dept. of Phys. & Med. Eng., Isfahan Univ. of Med. Sci., Isfahan, Iran
Abstract :
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.
Keywords :
biomedical transducers; cancer; least squares approximations; medical computing; quadratic programming; support vector machines; tumours; LS-SVM method; QP method; SMO-SVM method; learning method; linear support vector machine; malignant breast cancer; malignant cancer tumor detection; optimization algorithms; quadratic programming method; sequential minimal optimization; Accuracy; Breast cancer; Equations; Medical diagnostic imaging; Support vector machines; Tumors; Breast Cancer; Least Square SVM; Linear Support Vector Machine; Malignant Tumor; Sequential Minimal Optimization SVM;
Conference_Titel :
Instrumentation & Measurement, Sensor Network and Automation (IMSNA), 2012 International Symposium on
Conference_Location :
Sanya
Print_ISBN :
978-1-4673-2465-6
DOI :
10.1109/MSNA.2012.6324520
