Breast cancer tumor type recognition using graph feature selection technique and radial basis function neural network with optimal structure.
Context: Breast cancer is a major cause of mortality in young women in the developing countries. Early diagnosis is the key to improve survival rate in cancer patients.Aims: In this paper an intelligent system is proposed to breast cancer tumor type recognition.Settings and Design: The proposed syst...
| Publicado en: | Journal of Cancer Research & Therapeutics Vol. 14; no. 3; pp. 625 - 634 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Wolters Kluwer India Pvt Ltd
Apr-Jun2018
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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=130166649&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 130166649 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09731482 1BS3 jtl: Journal of Cancer Research & Therapeutics issn: 09731482 maglogo: N pubinfo: dt: Apr-Jun2018 vid: 14 iid: 3 pid: 16919 pub: Wolters Kluwer India Pvt Ltd artinfo: ui: 130166649 130166649 NLM29893330 10.4103/0973-1482.183561 NLM29893330 130166649 ppf: 625 ppct: 9 formats: tig: atl: Breast cancer tumor type recognition using graph feature selection technique and radial basis function neural network with optimal structure. aug: au: Zarbakhsh, Payam Addeh, Abdoljalil affil: Department of Electrical and Electronic Engineering, Eastern Mediterranean University, KKTC, Via Mersin-10, Gazimağusa sug: subj: Algorithms Breast Neoplasms Diagnosis Models, Statistical Diagnosis, Computer Assisted Methods Breast Neoplasms Classification Neural Networks (Computer) Resource Databases Information Science Prognosis Female Computer Simulation Arthritis Impact Measurement Scales Checklists Female ab: Context: Breast cancer is a major cause of mortality in young women in the developing countries. Early diagnosis is the key to improve survival rate in cancer patients.Aims: In this paper an intelligent system is proposed to breast cancer tumor type recognition.Settings and Design: The proposed system includes three main module: The feature selection module, the classifier module and the optimization module. Feature selection plays an important role in pattern recognition systems. The better selection of features usually results in higher accuracy rate.Methods and Material: In the proposed system we used a new graph based feature selection approach to select the best features. In the classifier module, the radial basis function neural network (RBFNN)is used as classifier. In RBF training, the number of RBFs and their respective centers and widths (Spread) have very important role in its performance. Therefore, artificial bee colony (ABC) algorithm is proposed for selecting appropriate parameters of the classifier.Statistical Analysis Used: The RBFNN with optimal structure and the selected feature classified the tumors with 99.59% accuracy.Results: The proposed system is tested on Wisconsin breast cancer database (WBCD) and the simulation results show that the recommended system exhibits a high accuracy.Conclusions: The proposed system has a high recognition accuracy and therefore we recommend the proposed system for breast cancer tumor type recognition. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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