A Survey on Computer- Aided Breast Cancer Detection Using Mammograms

Main Article Content

Riya Patankar
Dr. (Mrs.) Neeta A Deshpande


Breast cancer which mostly occurs in female is the second most common cancer in the world. The cause of the disease mostly remains unknown and therefore early detection and diagnosis is the only optimal solution to prevent tumor increase and allow a successful medical treatment, it also helps save lives at reduced cost. Cancer is a diseases driven by change in cells of the body and when they increase beyond normal growth and control.Mammography is the widely used technique for breast cancer detection is an x-ray of the breasts performed in the absence of symptoms. It can even detect very small sized tumors, even before they are tangible to symptoms. As a part of a screening program, mammography is currently the highly recommended method for early detection of breast cancer in women of age between 50 to 70 years. It can detect efficiently the very small tumors that generally have not yet formed metastases, and hence increase the chances of survival and recovery in women health. This screening method has been shown to be effective in reducing breast cancer mortality rates and have reduced mortality rates by 30–70%. Mammograms are difficult to interpret in the screening context. The sensitivity of mammography technique is affected by image quality and the radiologist’s level of expertise. Computer-aided diagnosis (CAD) process can improve the performance of radiologists to a high extent, by increasing the sensitivity to rates comparable to those obtained by double reading which makes process time consuming, in a cost-effective manner. This paper gives an overview of digital image processing along with pattern analysis techniques to address several areas in detection system of breast cancer, including the four stages of CAD system which are image preprocessing, image segmentation, features extraction and selection and image classification.


Keywords: Breast cancer, computer-aided diagnosis (CAD), mammography, classification, feature extraction, segmentation, ANN, GLCM

Article Details

How to Cite
Patankar, R., & Deshpande, D. (Mrs.) N. (2019). A Survey on Computer- Aided Breast Cancer Detection Using Mammograms. National Journal of Computer and Applied Science, 2(1), 1-6. Retrieved from http://www.njcas.co.in/index.php/njcas/article/view/17


[1] Madhuri Gupta, Bharat Gupta, A Comparative Study of Breast Cancer Diagnosis Using Supervised Machine Learning Techniques, IEEE Conference 2018

[2] Maxine Tan, Bin Zheng, Joseph K. Leader, and David Gur, Association between Changes in Mammographic Image Features and Risk for Near, IEEE Transaction on Medical Imaging, 2016

[3] Ahamed Lebbe Sayeth Saabith, Elankovan Sundararajan, Azuraliza Abu Bakar, Comparative study on different Classification techniques for Breast cancer datasetinternational Journal of Computer Science and Mobile Computing, Vol.3 Issue.10, October- 2014

[4] Nadia El Atlas, Mohammed El Aroussi, Mohammed Wahbi, Computer-Aided Breast Cancer Detection Using Mammograms: A Review, IEEE 2014

[5] Karthikeyan Ganesan, U. Rajendra Acharya, Chua Kuang Chua, Lim Choo Min, K. Thomas Abraham, and Kwan-Hoong Ng, Computer-Aided Breast Cancer Detection Using Mammograms: A Review, IEEE reviews in biomedical engineering, vol. 6, 2013

[6] Bin Zheng, Jules H. Sumkin, Margarita L. Zuley, Xingwei Wang, Amy H. Klym, David Gur, Bilateral mammographic density asymmetry and breast cancer risk: A preliminary assessment, Elsevier 2012

[7] Monika Sharma, R. B. Dubey, Sujata, S. K. Gupta, Feature Extraction of Mammograms, International Journal of Advanced Computer Research, 2012

[8] Snehal A. Mane, Dr. K. V. Kulhalli, Mammogram Image Features Extraction and Classification for Breast Cancer Detection, International Research Journal of Engineering and Technology (IRJET), Oct-2015

[9] R.Nithya, B.Santhi, Abnormality Comparitive Study On Feature Extraction Method For Breast Cancer Classification, Journal of Theoretical and Applied Information Technology 30th November 2011. Vol. 33 No.2

[10] Maxine Tan, Bin Zhen, Pandiyarajan Ramalingam, David Gur, Prediction of Near-term Breast Cancer Risk Based on Bilateral Mammographic Feature Asymmetry, Academic Radiology, Vol 20, No 12, December 2013

[11] Ramzi Chaieb, Amira Bacha, Image Features Extraction for Masses Classification in Mammograms, International Conference of Soft Computing and Pattern Recognition, IEEE 2014

[12] Wenqing Sun, Bin Zheng, Fleming Lure, Teresa Wu, Jianying Zhang, Benjamin Y. Wang, Edward C. Saltzstein, Wei Qian, Prediction of Near-term Risk of Developing Breast Cancer Using Computerized Features from Bilateral Mammograms