An Artificial Immune System-Based Support Vector Machine Approach for Classifying Ultrasound Breast Tumor Images.
A rapid and highly accurate diagnostic tool for distinguishing benign tumors from malignant ones is required owing to the high incidence of breast cancer. Although various computer-aided diagnosis (CAD) systems have been developed to interpret ultrasound images of breast tumors, feature selection an...
| Publicado en: | Journal of Digital Imaging Vol. 28; no. 5; pp. 576 - 586 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109465603&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109465603 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2015 vid: 28 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 109465603 109465603 109465603 10.1007/s10278-014-9757-1 NLM25561066 PMC4570897 109465603 ppf: 576 ppct: 10 formats: fmt: @attributes: type: P tig: atl: An Artificial Immune System-Based Support Vector Machine Approach for Classifying Ultrasound Breast Tumor Images. aug: au: Wu, Wen-Jie Lin, Shih-Wei Moon, Woo affil: Department of Information Management, Chang Gung University, Tao-Yuan 333 Republic of China sug: subj: Breast Neoplasms Diagnosis Breast Neoplasms Classification Breast Neoplasms Ultrasonography Diagnosis, Computer Assisted Radiographic Image Interpretation, Computer-Assisted Diagnosis, Differential Artificial Intelligence Immune System Algorithms Validation Studies Paired T-Tests Analysis of Variance ROC Curve Sensitivity and Specificity Predictive Value of Tests Human Funding Source ab: A rapid and highly accurate diagnostic tool for distinguishing benign tumors from malignant ones is required owing to the high incidence of breast cancer. Although various computer-aided diagnosis (CAD) systems have been developed to interpret ultrasound images of breast tumors, feature selection and the setting of parameters are still essential to classification accuracy and the minimization of computational complexity. This work develops a highly accurate CAD system that is based on a support vector machine (SVM) and the artificial immune system (AIS) algorithm for evaluating breast tumors. Experiments demonstrate that the accuracy of the proposed CAD system for classifying breast tumors is 96.67 %. The sensitivity, specificity, PPV, and NPV of the proposed CAD system are 96.67, 96.67, 95.60, and 97.48 %, respectively. The receiver operator characteristic (ROC) area index A is 0.9827. Hence, the proposed CAD system can reduce the number of biopsies and yield useful results that assist physicians in diagnosing breast tumors. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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