"Klasifikace karcinomu prsu z termogramu s pou\u017Eit\u00EDm statistick\u00FDch parametr\u016F a fuzzy klasifik\u00E1toru"@cs . "[2171B9CDDD37]" . "Breast Cancer Classification Using Statistical Features and Fuzzy Classification of Thermograms" . "Drastich, Ale\u0161" . . . "Breast Cancer Classification Using Statistical Features and Fuzzy Classification of Thermograms" . . "IEEE" . . "412241" . "RIV/00216305:26220/07:PU69164" . "Z(MSM0021630513)" . . "1-4244-1210-2" . "Z\u00E1vi\u0161ek, Michal" . . "2"^^ . "Klasifikace karcinomu prsu z termogramu s pou\u017Eit\u00EDm statistick\u00FDch parametr\u016F a fuzzy klasifik\u00E1toru"@cs . . "400"^^ . "2"^^ . "Statistick\u00E1 klasifikace termogramu prsu"@cs . . . . . "Breast Cancer Classification Using Statistical Features and Fuzzy Classification of Thermograms"@en . . "London UK" . "2007-07-23+02:00"^^ . "London UK" . . "Breast Cancer Classification Statistical Features Fuzzy Thermograms"@en . "26220" . . "1-400" . . "Breast Cancer Classification Using Statistical Features and Fuzzy Classification of Thermograms"@en . "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"@en . "RIV/00216305:26220/07:PU69164!RIV08-MSM-26220___" . "FUZZ-IEEE 2007 PROCEEDINGS" . "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" .