@article{(Open Science Index):https://publications.waset.org/pdf/10003918,
	  title     = {Calcification Classification in Mammograms Using Decision Trees},
	  author    = {S. Usha and  S. Arumugam},
	  country	= {},
	  institution	= {},
	  abstract     = {Cancer affects people globally with breast cancer being a leading killer. Breast cancer is due to the uncontrollable multiplication of cells resulting in a tumour or neoplasm. Tumours are called ‘benign’ when cancerous cells do not ravage other body tissues and ‘malignant’ if they do so. As mammography is an effective breast cancer detection tool at an early stage which is the most treatable stage it is the primary imaging modality for screening and diagnosis of this cancer type. This paper presents an automatic mammogram classification technique using wavelet and Gabor filter. Correlation feature selection is used to reduce the feature set and selected features are classified using different decision trees.
},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {9},
	  number    = {9},
	  year      = {2015},
	  pages     = {2120 - 2124},
	  ee        = {https://publications.waset.org/pdf/10003918},
	  url   	= {https://publications.waset.org/vol/105},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 105, 2015},
	}