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.
Gabriele Valvano REF proposed a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters.
Convolutional Neural Networks for the Segmentation of Microcalcification in Mammography Imaging
Journal of healthcare engineering
Journal of healthcare engineering
Computer Science Medicine