فهرست نشریات علمی دسترسی آزاد ایران

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. 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.