Convolutional Neural Networks for the segmentation of microcalcification in Mammography Imaging

TL;DR

This paper presents a convolutional neural network approach for detecting and segmenting microcalcification clusters in mammograms, achieving high accuracy and supporting radiologists in early breast cancer diagnosis.

Contribution

It introduces a novel CNN-based method specifically designed for microcalcification detection and segmentation in mammography images, demonstrating high accuracy with real clinical data.

Findings

98.22% accuracy in detecting suspect regions

97.47% accuracy in segmentation

Deep learning effectively supports radiologists

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 98.22% in the detection of preliminary suspect regions and of 97.47% in the segmentation task. Our results show how deep learning could be an effective tool to effectively support radiologists during mammograms examination.