Abstract: Breast cancer is the disease that most common malignancy affects female. It has been considered as a second most common leading cause of cancer death among other type of cancer, specifically in developing countries. Most of the previous researches in mammogram images achieved low classification accuracy that because of either inaccurate features or improper classifier methods. Mammography is the most effective method for detection of early breast cancer to increase the survival rate. The aim of this research is to Enhancement of Mammogram Images Classification Accuracy Using Data mining technique (decision tree classifier) for medical datasets classification that can aid the physician in a mammogram image classification as benign or malignant. The study the study methodology focuses on six phases starting with image collection, pre-processing (cutting images of the area of interest), feature extraction, feature selection, classification, and ending with testing and evaluation. Experimental results using a mammogram analysis dataset from Tumor therapy and Cancer Research Center, Shendi Sudan, showed that this approach achieves an accuracy of 97.04%.
Abstract: Breast cancer is the disease that most common malignancy affects female. It has been considered as a second most common leading cause of cancer death among other type of cancer, specifically in developing countries. Most of the previous researches in mammogram images achieved low classification accuracy that because of either inaccurate features or...Show More
