Breast cancer detection and classification using support vector machines and pulse coupled neural network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F12%3A86089246" target="_blank" >RIV/61989100:27240/12:86089246 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-642-31603-6_23" target="_blank" >http://dx.doi.org/10.1007/978-3-642-31603-6_23</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-642-31603-6_23" target="_blank" >10.1007/978-3-642-31603-6_23</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Breast cancer detection and classification using support vector machines and pulse coupled neural network

Popis výsledku v původním jazyce

This article introduces a hybrid scheme that combines the advantages of pulse coupled neural networks (PCNNs) and support vector machine, in conjunction with type-II fuzzy sets and wavelet to enhance the contrast of the original images and feature extraction. An application of MRI breast cancer imaging has been chosen and hybridization scheme have been applied to see their ability and accuracy to classify the breast cancer images into two outcomes: cancer or non-cancer. In order to enhance the contrastof the input image, identify the region of interest and detect the boundary of the breast pattern, a type-II fuzzy-based enhancement and PCNN-based segmentation were applied. Finally, wavelet-based features are extracted and normalized and a support vector machine classifier were employed to evaluate the ability of the lesion descriptors for discrimination of different regions of interest to determine whether they represent cancer or not. To evaluate the performance of presented approach

Název v anglickém jazyce

Breast cancer detection and classification using support vector machines and pulse coupled neural network

Popis výsledku anglicky

This article introduces a hybrid scheme that combines the advantages of pulse coupled neural networks (PCNNs) and support vector machine, in conjunction with type-II fuzzy sets and wavelet to enhance the contrast of the original images and feature extraction. An application of MRI breast cancer imaging has been chosen and hybridization scheme have been applied to see their ability and accuracy to classify the breast cancer images into two outcomes: cancer or non-cancer. In order to enhance the contrastof the input image, identify the region of interest and detect the boundary of the breast pattern, a type-II fuzzy-based enhancement and PCNN-based segmentation were applied. Finally, wavelet-based features are extracted and normalized and a support vector machine classifier were employed to evaluate the ability of the lesion descriptors for discrimination of different regions of interest to determine whether they represent cancer or not. To evaluate the performance of presented approach

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

2012

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in Intelligent Systems and Computing. Volume 179

ISBN

978-3-642-31602-9

ISSN

2194-5357

e-ISSN

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Počet stran výsledku

11

Strana od-do

269-279

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Praha

Datum konání akce

29. 8. 2011

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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