title: Tuning to optimize SVM approach for assisting ovarian cancer diagnosis with photoacoustic imaging.
abstract: Support vector machine (SVM) is one of the most effective classification methods for cancer detection. The efficiency and quality of a SVM classifier depends strongly on several important features and a set of proper parameters. Here, a series of classification analyses, with one set of photoacoustic data from ovarian tissues ex vivo and a widely used breast cancer dataset- the Wisconsin Diagnostic Breast Cancer (WDBC), revealed the different accuracy of a SVM classification in terms of the number of features used and the parameters selected. A pattern recognition system is proposed by means of SVM-Recursive Feature Elimination (RFE) with the Radial Basis Function (RBF) kernel. To improve the effectiveness and robustness of the system, an optimized tuning ensemble algorithm called as SVM-RFE(C) with correlation filter was implemented to quantify feature and parameter information based on cross validation. The proposed algorithm is first demonstrated outperforming SVM-RFE on WDBC. Then the best accuracy of 94.643% and sensitivity of 94.595% were achieved when using SVM-RFE(C) to test 57 new PAT data from 19 patients. The experiment results show that the classifier constructed with SVM-RFE(C) algorithm is able to learn additional information from new data and has significant potential in ovarian cancer diagnosis.
authors.affiliation: Key Laboratory of Precision Opto-Mechatronics Technology, Ministry of Education, The School of Istrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing, 100191, China.
authors.forename: Rui
authors.initials: R
authors.lastname: Wang
authors.affiliation: Key Laboratory of Precision Opto-Mechatronics Technology, Ministry of Education, The School of Istrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing, 100191, China.
authors.forename: Rui
authors.initials: R
authors.lastname: Li
authors.affiliation: Key Laboratory of Precision Opto-Mechatronics Technology, Ministry of Education, The School of Istrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing, 100191, China.
authors.forename: Yanyan
authors.initials: Y
authors.lastname: Lei
authors.affiliation: Department of Electrical and Computer Engineering, University of Connecticut, 371 Fairfield Way, U-2157, 06269 CT, Storrs, USA.
authors.forename: Quing
authors.initials: Q
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D005260:Female; D006801:Humans; D010051:Ovarian Neoplasms / Q000175:diagnosis*; D010053:Ovary / Q000473:pathology*; D061088:Photoacoustic Techniques / Q000379:methods*; D060388:Support Vector Machine
keywords: SVM-RFE; Support vector machines; correlation filter; feature selection; ovarian cancer detection