Abstract—Breast cancer continues to be a significant public health problem in the world and number one cause for death rate in Malaysia. Early detection is the key for improving breast cancer prognosis. Mammography is the most effective tool now available for an early diagnosis of breast cancer. However, the detection of cancer signs in mammograms is a difficult task due to irregular pathological structures and noise which are present in the image. It has been shown that in current breast cancer screenings 8%–20% of the tumors are missed by the radiologists. For this reason, a lot of research is currently being done to develop systems for computer aided detection to improve the accuracy. In this paper, review of mammogram mass detection and segmentation is focused. The main aim of the paper is to summarize and compares the method of mass detection in mammogram images. In specific, preprocessing, segmentation, feature extraction and classifications are discussed, Receiver operating curve and free-response receiver operating curve of each method is highlighted to show the sensitivity and specificity of the tumor detection.

Index Terms—Mammogram, preprocessing, segmentation,feature extraction and classification

Valliappan Raman is with Swinburne University of Technology SarawakCampus, Kuching, Malaysia, Phone: 006082260872 email:vraman@swinburne.edu.my.
Putra Sumari is with University Sains Malaysia, Penang, Malaysia. Email:putras@cs.us.my.
H.H.Then is with Swinburne University of Technology Sarawak,Kuching, Malaysia. Email: pthen@swinburne.edu.my.
Saleh Ali Omari is with University Sains Malaysia, Penang, and Malysia.Email: salehalomari2005@yahoo.com.

Cite: Valliappan Raman, Putra Sumari, H.H.Then, and Saleh Ali K. Al-Omari, "Review on Mammogram Mass Detection by Machine Learning Techniques," International Journal of Computer and Electrical Engineering vol. 3, no. 6, pp. 873-879, 2011.