Abstract:
Breast cancer is the most common malignancy disease that affects female population and the number of affected people is the second most common leading cause of cancer deaths among all cancer types in the developing countries. Mammography is the most effective method for detection of early breast cancer to increase the survival rate. This paper presented the classification method for mammogram Image using the decision tree techniques. Three measures were used to evaluate performance in terms of accuracy, sensitivity, and privacy. The aim of the study is to determine the best decision tree classifier for medical datasets classification. The study emphasizes five phases; starting with collecting images, pre-processing (image cropping of ROI), features extracting, classification and end with testing and evaluating. Experimental results show that Random Forest has a better performance than ID3, J48
Description:
Breast cancer is the most common malignancy disease that affects female population and the number of affected people is the second most common leading cause of cancer deaths among all cancer types in the developing countries. Mammography is the most effective method for detection of early breast cancer to increase the survival rate. This paper presented the classification method for mammogram Image using the decision tree techniques. Three measures were used to evaluate performance in terms of accuracy, sensitivity, and privacy. The aim of the study is to determine the best decision tree classifier for medical datasets classification. The study emphasizes five phases; starting with collecting images, pre-processing (image cropping of ROI), features extracting, classification and end with testing and evaluating. Experimental results show that Random Forest has a better performance than ID3, J48
