Abstract

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Title:: True- and false-positive detections of breast microcalcifications based on Adaboost-decision tree algorithm

Author(s):: SHEN Nan1; XING Suxia1; HE Xiangping2; PAN Ziyan1; WANG Yu1; 1. School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China 2. Breast Disease Prevention and Control Center, Haidian Maternal and Child Health Hospital, Beijing 100080, China

Keywords:: Keywords: breast cancer Adaboost-decision tree microcalcification Haralick texture feature grey-level run length matrix

Abstract:: Abstract: The early manifestation of breast cancer is mainly characterized by microcalcifications in mammograms. The true- and false-positive detections of microcalcifications are of great significance for the early screening of breast cancer. DDSM images were selected for the experiment, and 400 suspected calcification regions were manually intercepted. The feature set was firstly established by extracting Haralick texture features and grey-level run length matrix features of all regions and then, Adaboost algorithm was integrated with decision tree to construct a strong classifier AB-DT for classifying 400 suspected calcification regions. It was found that the model classification performance was the best when 462 decision trees were integrated. Finally, 10-fold cross-validation was conducted, and the results revealed that the accuracy, sensitivity and specificity of AB-DT algorithm reached 91.75%, 91.75% and 91.79%, respectively, and that F1 score was 0.918 7. The proposed model has superior performance in the true- and false-positive detections of microcalcifications, and it can be used to assist the detection of breast microcalcifications, which has certain clinical application value.