A novel and reliable computational intelligence system for breast cancer detection.
Cancer is the second important morbidity and mortality factor among women and the most incident type is breast cancer. This paper suggests a hybrid computational intelligence model based on unsupervised and supervised learning techniques, i.e., self-organizing map (SOM) and complex-valued neural net...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 56; no. 5; pp. 721 - 733 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
May2018
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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=129156211&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 129156211 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: May2018 vid: 56 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 129156211 129156211 NLM28891042 10.1007/s11517-017-1721-z NLM28891042 129156211 ppf: 721 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A novel and reliable computational intelligence system for breast cancer detection. aug: au: Zadeh Shirazi, Amin Seyyed Mahdavi Chabok, Seyyed Javad Mohammadi, Zahra affil: Department of Artificial Intelligence, Islamic Azad University, Mashhad Branch, Mashhad, Iran sug: subj: Artificial Intelligence Early Detection of Cancer Breast Neoplasms Diagnosis Models, Theoretical Reproducibility of Results ROC Curve Decision Making Female Female ab: Cancer is the second important morbidity and mortality factor among women and the most incident type is breast cancer. This paper suggests a hybrid computational intelligence model based on unsupervised and supervised learning techniques, i.e., self-organizing map (SOM) and complex-valued neural network (CVNN), for reliable detection of breast cancer. The dataset used in this paper consists of 822 patients with five features (patient's breast mass shape, margin, density, patient's age, and Breast Imaging Reporting and Data System assessment). The proposed model was used for the first time and can be categorized in two stages. In the first stage, considering the input features, SOM technique was used to cluster the patients with the most similarity. Then, in the second stage, for each cluster, the patient's features were applied to complex-valued neural network and dealt with to classify breast cancer severity (benign or malign). The obtained results corresponding to each patient were compared to the medical diagnosis results using receiver operating characteristic analyses and confusion matrix. In the testing phase, health and disease detection ratios were 94 and 95%, respectively. Accordingly, the superiority of the proposed model was proved and can be used for reliable and robust detection of breast cancer. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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