Microcalcifications are an early mammographic indicator of breast cancer. To assist screening radiologists in reading mammograms, machine learning techniques have been developed for the automated detection of microcalcifications. In the last few years, Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in many computer vision and medical image analysis applications. A key step in CNN-based detection is image preprocessing, including brightness and contrast variations. In this work, we investigate the influence of preprocessing of digital mammograms on the microcalcification detection performance of two CNNs inspired by the popular AlexNet and VGGnet. We tested two preprocessing methods commonly applied to unprocessed raw digital mammograms: (i) the logarithmic transformation adopted by different manufacturers for the presentation of the image to the radiologists; and (ii) the square-root of image intensity that stabilizes the intensity-dependent noise present in the mammogram. Experiments were performed on 1,066 mammograms acquired with GE Senographe systems. Both preprocessing methods yielded statistically significantly better microcalcification detection performance. Results of the square-root transform were superior to those obtained with the log transform.
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