An interval prototype classifier based on a parameterized distance applied to breast thermographic images.
Breast cancer is one of the leading causes of death in women. Because of this, thermographic images have received a refocus for diagnosing this cancer type. This work proposes an innovative approach to classify breast abnormalities (malignant, benignant and cyst), employing interval temperature data...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 55; no. 6; pp. 873 - 885 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jun2017
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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=123190315&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 123190315 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jun2017 vid: 55 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 123190315 123190315 144102795 NLM27629552 10.1007/s11517-016-1565-y NLM27629552 123190315 ppf: 873 ppct: 12 formats: fmt: @attributes: type: P tig: atl: An interval prototype classifier based on a parameterized distance applied to breast thermographic images. aug: au: Araújo, Marcus Souza, Renata Lima, Rita Filho, Telmo Araújo, Marcus C Souza, Renata M C R Lima, Rita C F Filho, Telmo M Silva affil: Departamento de Engenharia Mecânica , Universidade Federal de Pernambuco , Av. Prof. Moraes Rego, 1235, Cidade Universitária Recife 50670901 Brazil sug: subj: Breast Neoplasms Diagnosis Breast Pathology Breast Neoplasms Pathology Female Thermography Methods Sensitivity and Specificity Brazil Algorithms Temperature Scales Female ab: Breast cancer is one of the leading causes of death in women. Because of this, thermographic images have received a refocus for diagnosing this cancer type. This work proposes an innovative approach to classify breast abnormalities (malignant, benignant and cyst), employing interval temperature data in order to detect breast cancer. The learning step takes into account the internal variation of the intervals when describing breast abnormalities and uses a way to map these intervals into a space where they can be more easily separated. The method builds class prototypes, and the allocation step is based on a parameterized Mahalanobis distance for interval-valued data. The proposed classifier is applied to a breast thermography dataset from Brazil with 50 patients. We investigate two different scenarios for parameter configuration. The first scenario focuses on the overall misclassification rate and achieves 16 % misclassification rate and 93 % sensitivity to the malignant class. The second scenario maximizes the sensitivity to the malignant class, achieving 100 % sensitivity to this specific class, along with 20 % overall misclassification rate. We compare the performances of our approach and of many methods taken from the literature of interval data classification for the breast thermography task. Results show that our method outperforms competing algorithms. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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