Classification Mammogram Images Using ID3 decision tree Algorithm Based on Contourlet Transform - Matheel Emaduldeen Abdulmunim; Zainab Fawzy Abed (Engineering and Technology Journal, 2015)
Breast cancer is the most common malignancy of women and is the second most common and leading cause of cancer deaths among them. At present, there are no effective ways to prevent breast cancer, because its cause is not yet fully known. Early detection is an effective way to diagnose and manage breast cancer can give a better chance of full recovery. Therefore, early detection of breast cancer can play an important role in reducing the associated morbidity and mortality rates. In this paper, using contourlet transform that can capture the intrinsic geometrical structure that is key in visual information. The contourlet expansion is composed of basis images oriented in various directions in multiple scales, with flexible aspect ratios. The basic idea of this paper is to design and implement a proposed system that can aid the physician in reading a mammogram image by study the usage of wavelet and contourlet transform based on various operations on mammogram images and classifies them as normal, benign or malignant based on the decision tree ID3 algorithm. The experimental results show that the ID3 classifier achieves accuracy of 81% in the case of wavelet transform and 95% with contourlet transform for the same number of the test set.