MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
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Malignant breast tumors and benign masses appear in mammograms with different shape characteristics: the former usually have rough, spiculated, or microlobulated contours, whereas the latter commonly have smooth, round, oval, or macrolobulated contours. Visual features that characterize shape roughness can assist in distinguishing between malignant tumors and benign masses in mammo-grams. Here we propose a new approach based on Hilbert curves to classify breast masses as benign or malignant. The feature extraction is performed in linear time, and is amenable to parallel processing, whereas the classification phase can be performed by a classical neural networks structure. We evaluated our method using a set of 111 contours from 65 benign masses and 46 malignant tumors. As the experimental evaluations show, we achieved an accuracy of 0.99 in terms of the area under the receiver operating characteristics curve, a very high classification result.