In this paper, we propose a cloud based decision support system for screening breast cancer using digital mammograms. The proposed system is deployed in a private cloud as software / infrastructure as a service. The combination of image enhancement techniques, feature extraction techniques, feature selection techniques, ensemble neural networks for classification, results verification process and deployment in the private cloud are added advantages for effective performance of the system.

1. Breast Cancer What Are the Key Statistics for Breast Cancer?. American Cancer Society Cancer Resource Information. http://www. cancer. org
2. Harirchi, et al. , ¯Breast cancer in Iran: a review of 903 case records,. Public Health, 2000. 114(2): p. 143-145.
3. Subashini. T, Ramalingam. V, Palanivel. S, 2009, ¯Breast mass classification based on cytological patterns using RBFNN and SVM,. Expert Systems with Applications, 36(3): p. 5284-5290.
4. Sariego J, 2010. "Breast cancer in the young patient". The American surgeon, 76 (12), pp 1397–1401
5. Kekre HB, Sarode Tanuja K and Gharge Saylee M, 2009, "Tumor Detection in Mammography Images using Vector Quantization Technique", International Journal of Intelligent Information Technology Application, 2(5):237-242.
6. Baines CJ, McFarlane DV, Miller AB, 1990, "The role of the reference radiologist: Estimates of interobserver agreement and potential delay in cancer detection in the national screening study". Invest Radiol, 25: 971-076.

Breast Cancer Digital Mammograms Neural Networks Ada Boost Feature Extraction Feature Selection Cloud Computing