Abstract
Cluster of microcalcifications can be an early sign of breast cancer. In this paper, we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work, we used 283 mammograms to train and validate our model, obtaining an accuracy of 99.99% on microcalcification detection and a false positive rate of 0.005%. Our results show how deep learning could be an effective tool to effectively support radiologists during mammograms examination.
Highlights
Breast cancer is one of the most common malignant neoplasms in the female population. e referral examination used for screening of breast cancer is mammography.Mammography is a radiological procedure that uses a bundle of X photons to map the breast tissue attenuation
Due to the intrinsic limitations of classical methods, with this work, we propose a system based on deep learning [4], demonstrating the potentialities of convolutional neural networks (CNNs) to effectively detect and segment breast microcalcifications to support radiologists in mammograms examination
Since most classical methods suffer from a high false positive rate (FPR) in the MC detection process, we calculated the FPR obtained from our system
Summary
Breast cancer is one of the most common malignant neoplasms in the female population. e referral examination used for screening of breast cancer is mammography.Mammography is a radiological procedure that uses a bundle of X photons to map the breast tissue attenuation. Breast cancer is one of the most common malignant neoplasms in the female population. E referral examination used for screening of breast cancer is mammography. E Breast Imaging Reporting and Data System (BIRADS) standardized the interpretation of MCs by defining a scale ranging from 2 (benign finding) to 5 (highly suspicious of malignancy) based on their shape, density, and distribution within the breast. An important type of benign calcification that can be seen incidentally on mammography is breast arterial calcification (BAC), which seems to correlate with coronary calcification. Breast vascular calcifications are differentiated from malignant and ductal calcifications by size, morphology, and distribution and appear as linear “tram tracks” [1] of calcification along arterial walls with a winding rather than branching course on mammography
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