Combining Multi-classifier with CNN in Detection and Classification of Breast Calcification

Breast calcification or microtumors screening can early detect breast cancer that can make the disease easier to treat. At present, the segmentation of breast calcifications relies on the delineate by doctors. The process is time-consuming, and the benefits are not readily apparent. None of the paper has been discussed on combining automatically delineate and classify the breast calcifications to benign or malignant in previous research. According to the above reasons, we proposed an approach on combining Cascade Adaboost with CNN to delineate breast calcifications in mammogram and classify breast calcifications to benign or malignant by the CNN we trained. The ability of classification in Cascade Adaboost algorithm is better than Adaboost algorithm, it can significantly reduce the time cost by classification in CNN and speed up the process time. In this paper, we compare our method with the architecture of R-CNN combining CNN, and the experimental results show that by using Cascade Adaboost combined with CNN can detect calcification more accurately and classify it into benign or malignant. We hope that by using the approach in this work can help doctors to detect and diagnose breast calcifications in less time.