Published Paper Details:

Abstract � Breast cancer is genetically and clinically heterogeneous disease. An area of interest for data mining applications is the study of biomedical data which is the combination of image processing with the data mining techniques or algorithm to analyze the hidden data by creating the patterns. This paper deals with the classification of breast cancer within digital mammography images. Digital Image processing facilitates in medical area widely in case of detecting and classifying the mammogram images. Classification of breast cancer in mammogram image is an important step to classify whether the patient is affected by cancerous (malignant) or non-cancerous (benign) tumors. Researchers have been used various algorithms and investigation methods to investigate mammogram images depending on the ultimatum of the disease, status of the disease and the quality of image. The main aim of classification of mammogram images is to select the best treatment. Doctors suggest best treatment for the patient if the result is positive and can avoid from death. Researchers uses so many data mining algorithms used for classification of mammogram images are k-Nearest Neighbor, Nave Bayes, Artificial Neural Network and Support Vector Machine algorithm.