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Statements

Subject Item
n2:RIV%2F00216305%3A26220%2F07%3APU69164%21RIV08-MSM-26220___
rdf:type
skos:Concept n18:Vysledek
dcterms:description
Statistická klasifikace termogramu prsu Advances in camera technologies and reduced equipment costs have lead to an increased interest in the application of thermography in the medical fields. Thermography is of particular interest for detection of breast cancer as it has been shown that it is capable of detecting the cancer earlier and is also allows diagnosis of fatty breast tissue. In this paper we perform breast cancer detection based on thermography, using a series of statistical features extracted from the thermograms coupled with a fuzzy rule-based classification system for diagnosis. The features stem from a comparison of left and right breast areas and quantify the bilateral differences encountered. Following this asymmetry analysis the features are fed to a fuzzy classification system. This classifier is used to extract fuzzy if-then rules based on a training set of known cases. Experimental results on a set of nearly 150 cases show the proposed system to work well accurately classifying about 80% of cases, a performance that is c Advances in camera technologies and reduced equipment costs have lead to an increased interest in the application of thermography in the medical fields. Thermography is of particular interest for detection of breast cancer as it has been shown that it is capable of detecting the cancer earlier and is also allows diagnosis of fatty breast tissue. In this paper we perform breast cancer detection based on thermography, using a series of statistical features extracted from the thermograms coupled with a fuzzy rule-based classification system for diagnosis. The features stem from a comparison of left and right breast areas and quantify the bilateral differences encountered. Following this asymmetry analysis the features are fed to a fuzzy classification system. This classifier is used to extract fuzzy if-then rules based on a training set of known cases. Experimental results on a set of nearly 150 cases show the proposed system to work well accurately classifying about 80% of cases, a performance that is c
dcterms:title
Breast Cancer Classification Using Statistical Features and Fuzzy Classification of Thermograms Klasifikace karcinomu prsu z termogramu s použitím statistických parametrů a fuzzy klasifikátoru Breast Cancer Classification Using Statistical Features and Fuzzy Classification of Thermograms
skos:prefLabel
Klasifikace karcinomu prsu z termogramu s použitím statistických parametrů a fuzzy klasifikátoru Breast Cancer Classification Using Statistical Features and Fuzzy Classification of Thermograms Breast Cancer Classification Using Statistical Features and Fuzzy Classification of Thermograms
skos:notation
RIV/00216305:26220/07:PU69164!RIV08-MSM-26220___
n4:strany
1-400
n4:aktivita
n16:Z
n4:aktivity
Z(MSM0021630513)
n4:dodaniDat
n17:2008
n4:domaciTvurceVysledku
n6:8355681 n6:9453164
n4:druhVysledku
n8:D
n4:duvernostUdaju
n15:S
n4:entitaPredkladatele
n5:predkladatel
n4:idSjednocenehoVysledku
412241
n4:idVysledku
RIV/00216305:26220/07:PU69164
n4:jazykVysledku
n10:eng
n4:klicovaSlova
Breast Cancer Classification Statistical Features Fuzzy Thermograms
n4:klicoveSlovo
n11:Breast%20Cancer%20Classification%20Statistical%20Features%20Fuzzy%20Thermograms
n4:kontrolniKodProRIV
[2171B9CDDD37]
n4:mistoKonaniAkce
London UK
n4:mistoVydani
London UK
n4:nazevZdroje
FUZZ-IEEE 2007 PROCEEDINGS
n4:obor
n13:JA
n4:pocetDomacichTvurcuVysledku
2
n4:pocetTvurcuVysledku
2
n4:rokUplatneniVysledku
n17:2007
n4:tvurceVysledku
Drastich, Aleš Závišek, Michal
n4:typAkce
n21:WRD
n4:zahajeniAkce
2007-07-23+02:00
n4:zamer
n19:MSM0021630513
s:numberOfPages
400
n9:hasPublisher
IEEE
n12:isbn
1-4244-1210-2
n20:organizacniJednotka
26220