Abstract: The main problem that corresponding with breast cancer is how to deal with small calcification part inside the breast called microcalcification (MC). A breast screening examination called mammogram is provided as preventive way. Mammogram image with a considerable amount of MC or called clustered MC has been a problem for the doctor and the radiologist. Particularly, when they should determine correctly the region of interest. This work is an improvement work from the previous work. It utilizes the Daubechies D4 wavelet as a feature extractor and the SVM classifier as an effective binary classifier. The escalating point shown with 84.44% of classification performance, 90% of sensitivity and 91.43% of specificity.

Kohei Arai, Indra Nugraha Abdullah, Hiroshi Okumura and Rie Kawakami, “Improvement of Automated Detection Method for Clustered Microcalcification Based on Wavelet Transformation and Support Vector Machine ” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 2(4), 2013. http://dx.doi.org/10.14569/IJARAI.2013.020405

@article{Arai2013,
title = {Improvement of Automated Detection Method for Clustered Microcalcification Based on Wavelet Transformation and Support Vector Machine },
journal = {International Journal of Advanced Research in Artificial Intelligence}
doi = {10.14569/IJARAI.2013.020405},
url = {http://dx.doi.org/10.14569/IJARAI.2013.020405},
year = {2013},
publisher = {The Science and Information Organization},
volume = {2},
number = {4},
author = {Kohei Arai and Indra Nugraha Abdullah and Hiroshi Okumura and Rie Kawakami},
}