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Masses are one of the prevalent early signs of breast cancer, visible in mammogram. However, its variation in shape, size, and appearance often creates hazards in proper diagnosis of mammographic masses. This study analyses the 2D singularities of masses and their surrounding regions with Ripplet-II transform to classify them as benign and malignant. Since benign and malignant masses may change the...
In the analysis of digital or digitized mammographic images, a requirement is to learn to separate benign abnormalities from malignant ones. Such an activity could form part of a computer-aided diagnosis (CAD) tool. We present a CAD study of mass and calcification lesions found in digital database of screening mammography (DDSM) using BI-RADS-based features to demonstrate the performance of feature...
The purpose of this work is to compare the performance of support vector machines (SVM) and multi-layer perceptron (MLP) in the task of detection and diagnosis of microcalcification clusters in mammograms (MCCs). As data source, the "digital database for screening mammography"; (DDSM) was used. The results show a similar performance for SVM and MLP, in both tasks, detection and diagnosis...
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