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According to the opinions of specialized doctors, being able to accurately classify breast calcification is very important, and with this information available, only then medical treatments can be applied properly. However, for any delay treatment or misdiagnosis, it is very likely as the key attributed to the fatal death of the patients. Currently, there are a lot of researches on the development of many methods with application of mammographic for classification of breast calcification out there already. However, in this paper, mammography image is used to classify breast calcification. And, we use a cubic curve contrast enhancement method to enhance image contrast. Next, we use Gabor wavelet and Fractal-based to extract texture feature on the breast image. Finally, we further to input these features into Back-propagation neural network method for classification. Next, we will make classification of these different features, as well as to put a link among them, in order to get better accuracy for classification. Experimental result shows our method has a good accuracy, and be able to precisely help the doctors for recognizing the breast calcification whether they are good or not. In addition, it can be verified that the accuracy rate of our method is up to 89.55%.

The Classification of Breast Calcification using Texture Feature of Fractal-based and Gabor Wavelet-based