title: An expert support system for breast cancer diagnosis using color wavelet features.
abstract: Breast cancer diagnosis can be done through the pathologic assessments of breast tissue samples such as core needle biopsy technique. The result of analysis on this sample by pathologist is crucial for breast cancer patient. In this paper, nucleus of tissue samples are investigated after decomposition by means of the Log-Gabor wavelet on HSV color domain and an algorithm is developed to compute the color wavelet features. These features are used for breast cancer diagnosis using Support Vector Machine (SVM) classifier algorithm. The ability of properly trained SVM is to correctly classify patterns and make them particularly suitable for use in an expert system that aids in the diagnosis of cancer tissue samples. The results are compared with other multivariate classifiers such as Naïves Bayes classifier and Artificial Neural Network. The overall accuracy of the proposed method using SVM classifier will be further useful for automation in cancer diagnosis.
authors: Department of Electronics and Communication Engineering, National Institute of Technology, Tiruchirappalli, India. issacniwas@gmail.com
authors: S
authors: S
authors: Issac Niwas
authors: P
authors: P
authors: Palanisamy
authors: Rajni
authors: R
authors: Chibbar
authors: W J
authors: WJ
authors: Zhang
mesh_terms: D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000473:pathology; D048430:Cell Shape; D048429:Cell Size; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016571:Neural Networks, Computer; D011859:Radiography; D060388:Support Vector Machine; D058067:Wavelet Analysis