Microcalcification Detection in Mammograms Using Deep Learning

Abstract

Background: Mammography is the most reliable and popular method in the clinical diagnosis of breast cancer. Calcifications are subtle lesions in mammograms that can be cancerous and difficult to detect for radiologists. Computer-aided detection (CAD) can help radiologists identify malignant lesions. Objectives: This study aimed to propose a deep learning based CAD system for detecting calcifications in mammograms. Patients and Methods: A total of 815 in-house mammograms were collected from 204 women undergoing screening mammography. Calcifications in the mammograms were annotated by specialists. Each mammogram was divided into patches of fixed size, and then, patches containing calcifications were extracted, along with the same number of normal patches. A ResNet-50 Convolutional Neural Network (CNN) was trained for classification of patches into normal and calcification groups using training data and then the performance of the trained CNN was tested with new test data. Results: The proposed patch learning approach (PLA) showed a classification accuracy of 96.7% in the binary classification of patches. Therefore, it could detect calcification regions in a given mammogram. The PLA achieved sensitivity and specificity of 96.7% and 96.7%, respectively, with an area under the curve of 98.8%. Conclusion: The present results highlighted the efficacy of the proposed PLA, especially for limited training data. Direct comparison with previous studies is not possible due to differences in datasets. Nevertheless, the PLA accuracy in detecting calcifications was higher than that of deep learning based CAD systems in previous studies. The effective performance of PLA may be attributed to the manual removal of uninformative patches, as they were not used in the training set.