Thermogram breast cancer prediction approach based on Neutrosophic sets and fuzzy c-means algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86096564" target="_blank" >RIV/61989100:27240/15:86096564 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7319334" target="_blank" >http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7319334</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/EMBC.2015.7319334" target="_blank" >10.1109/EMBC.2015.7319334</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Thermogram breast cancer prediction approach based on Neutrosophic sets and fuzzy c-means algorithm

Popis výsledku v původním jazyce

The early detection of breast cancer makes many women survive. In this paper, a CAD system classifying breast cancer thermograms to normal and abnormal is proposed. This approach consists of two main phases: automatic segmentation and classification. Forthe former phase, an improved segmentation approach based on both Neutrosophic sets (NS) and optimized Fast Fuzzy c-mean (F-FCM) algorithm was proposed. Also, post-segmentation process was suggested to segment breast parenchyma (i.e. ROI) from thermogram images. For the classification, different kernel functions of the Support Vector Machine (SVM) were used to classify breast parenchyma into normal or abnormal cases. Using benchmark database, the proposed CAD system was evaluated based on precision, recall, and accuracy as well as a comparison with related work. The experimental results showed that our system would be a very promising step toward automatic diagnosis of breast cancer using thermograms as the accuracy reached 100%. (C) 2

Název v anglickém jazyce

Thermogram breast cancer prediction approach based on Neutrosophic sets and fuzzy c-means algorithm

Popis výsledku anglicky

The early detection of breast cancer makes many women survive. In this paper, a CAD system classifying breast cancer thermograms to normal and abnormal is proposed. This approach consists of two main phases: automatic segmentation and classification. Forthe former phase, an improved segmentation approach based on both Neutrosophic sets (NS) and optimized Fast Fuzzy c-mean (F-FCM) algorithm was proposed. Also, post-segmentation process was suggested to segment breast parenchyma (i.e. ROI) from thermogram images. For the classification, different kernel functions of the Support Vector Machine (SVM) were used to classify breast parenchyma into normal or abnormal cases. Using benchmark database, the proposed CAD system was evaluated based on precision, recall, and accuracy as well as a comparison with related work. The experimental results showed that our system would be a very promising step toward automatic diagnosis of breast cancer using thermograms as the accuracy reached 100%. (C) 2

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. Volume 2015-November

ISBN

978-1-4244-9271-8

ISSN

1557-170X

e-ISSN

—

Počet stran výsledku

4

Strana od-do

4254-4257

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Miláno

Datum konání akce

25. 8. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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