Prediction of breast cancer in mammagram image using support vector machine and fuzzy C-means
G. S. Banu, Andamjath Fareeth, Nisar Hundewale · 2012
Breast cancer is the leading cause of non preventable cancer death among women. A typical mammogram is an intensity X-ray image with gray levels showing levels of contrast inside the breast that which characterize normal tissue and different calcifications and masses. Analyzing an X-ray mammogram is challenging because of the similarities of cancer growth with other tissue growth. Therefore, it poses inaccuracy in identifying the presence of breast cancer. Now a day, detection of calcifications in mammograms has received much attention from researchers and public health practitioners. In this paper, we propose a novel technique that uses continuous wavelet transform (1D - CWT) as feature selection technique and support vector machine (SVM) as classifier. Our experimental result achieved excellent classification accuracy (100%) and compared with the other technique (1D - CWT and Fuzzy-C-mean clustering).