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Classification of benign and malignant masses in mammograms is one of the most difficult tasks in development of mammographic computer-aided diagnosis (CAD) system. This paper presents a deep learning-based method that utilizes a deep convolutional neural network (DCNN) to classify mammographic masses into two classes: benign and malignant masses. In order to train the DCNN for mass classification,...
We propose a new method for accurate detection of architectural distortion that is a typical sign of breast cancer lesions in mammograms and necessary to be detected and diagnosed properly at an early stage for improvement of the survival rate of patients. An essential core of the proposed method is to efficiently extract a new general feature of the architectural distortions whose lesional intensities...
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