Classification Method for Mammogram Image Using the Decision Tree Techniques

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dc.contributor.author Hassan Abdalla, Ahmed Ali
dc.contributor.author Mohamed Alhag, Alobed
dc.date.accessioned 2018-11-25T15:08:20Z
dc.date.available 2018-11-25T15:08:20Z
dc.date.issued 2018-11-01
dc.identifier.issn 2984-8628
dc.identifier.uri http://hdl.handle.net/123456789/490
dc.description Breast cancer is the most common malignancy disease that affects female population and the number of affected people is the second most common leading cause of cancer deaths among all cancer types in the developing countries. Mammography is the most effective method for detection of early breast cancer to increase the survival rate. This paper presented the classification method for mammogram Image using the decision tree techniques. Three measures were used to evaluate performance in terms of accuracy, sensitivity, and privacy. The aim of the study is to determine the best decision tree classifier for medical datasets classification. The study emphasizes five phases; starting with collecting images, pre-processing (image cropping of ROI), features extracting, classification and end with testing and evaluating. Experimental results show that Random Forest has a better performance than ID3, J48 en_US
dc.description.abstract Breast cancer is the most common malignancy disease that affects female population and the number of affected people is the second most common leading cause of cancer deaths among all cancer types in the developing countries. Mammography is the most effective method for detection of early breast cancer to increase the survival rate. This paper presented the classification method for mammogram Image using the decision tree techniques. Three measures were used to evaluate performance in terms of accuracy, sensitivity, and privacy. The aim of the study is to determine the best decision tree classifier for medical datasets classification. The study emphasizes five phases; starting with collecting images, pre-processing (image cropping of ROI), features extracting, classification and end with testing and evaluating. Experimental results show that Random Forest has a better performance than ID3, J48 en_US
dc.description.sponsorship Shendi University en_US
dc.language.iso en en_US
dc.publisher Donnish Journal of Mathematics and Computer Science Research en_US
dc.relation.ispartofseries Vol. 4(1);pp. 001-005 November, 2018
dc.subject Mammograms en_US
dc.subject Mammograms en_US
dc.subject Decision Tree en_US
dc.subject Early detection en_US
dc.subject Image classification en_US
dc.title Classification Method for Mammogram Image Using the Decision Tree Techniques en_US
dc.type Article en_US


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