Research Article Breast Microcalcification Diagnosis Using Deep Convolutional Neural Network from Digital Mammograms Hongmin Cai , 1,2 Qinjian Huang, 1 Wentao Rong, 1 Yan Song, 1 Jiao Li, 3 Jinhua Wang, 4 Jiazhou Chen, 1 andLiLi 4 1 School of Computer Science and Engineering, South China University of Technology, Guangzhou 510000, China 2 Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information, South China University of Technology, Guangzhou, China 3 Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong 510060, China 4 Medical Imaging Center, Shenzhen Hospital of Southern Medical University, Shenzhen, Guangdong 518101, China Correspondence should be addressed to Hongmin Cai; hmcai@scut.edu.cn Received 31 October 2018; Revised 22 December 2018; Accepted 4 February 2019; Published 3 March 2019 Academic Editor: Cristiana Corsi Copyright©2019HongminCaietal.isisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Mammography is successfully used as an effective screening tool for cancer diagnosis. A calcification cluster on mam- mography is a primary sign of cancer. Early researches have proved the diagnostic value of the calcification, yet their performanceishighlydependentonhandcraftedimagedescriptors.Characterizingthecalcificationmammographyinan automatic and robust way remains a challenge. In this paper, the calcification was characterized by descriptors obtained fromdeeplearningandhandcrafteddescriptors.Wecomparedtheperformancesofdifferentimagefeaturesetsondigital mammograms. e feature sets included the deep features alone, the handcrafted features, their combination, and thefiltereddeepfeatures.Experimentalresultshavedemonstratedthatthedeepfeaturesoutperformhandcraftedfeatures, but the handcrafted features can provide complementary information for deep features. We achieved a classification precisionof89.32%andsensitivityof86.89%usingthefiltereddeepfeatures,whichisthebestperformanceamongallthe feature sets. 1.Introduction Breastcanceristhemostcommoncanceraffectingwomen’s health. Early detection of breast cancer has been shown to increase the survival rate, thereby significantly increasing patients’ lifespan [1]. Mammography is a very popular noninvasiveimagingtoolwithlowcostcomparedwithother advanced equipment, such as computed tomography. It is widelyusedtodiagnosebreastdiseaseatanearlystagedueto its high sensitivity. erefore, it is frequently used as a tool for early screening. Duringmammographyscreening,thepresenceofbreast microcalcifications (MCs) is a primary risk factor for breast cancer. Breast calcifications in the early stages of breast cancer appear like scattered spots in the mammographic image that range from 0.1 to 1.0mm in size [2]. Previous studies have found that MCs associated with malignant lesionstendtobesmallerinsize,greaterinamount,andare more densely distributed since they occur within the milk ductsandotherassociatedstructuresinthebreastandfollow theductalanatomy[3].Becauseahighcorrelationhasbeen observed between the appearance of calcification clusters and pathology results, the MCs provide a standard and effective way for the automated detection of breast tumors. Besides, large-scale genome-wide association studies (GWAS) have proved to be a strong support for identifying disease risk pathways [4]. Experimental results provide clinicallyusefulcluesaboutthelinkbetweentheseriskgenes and MS susceptibility in the Chinese population. e study of [5] demonstrates convincingly that the genetic pre- dispositionfordevelopmentofADisrootedintheimmune system, rather than in neuronal cells in some degree. Hindawi Computational and Mathematical Methods in Medicine Volume 2019, Article ID 2717454, 10 pages https://doi.org/10.1155/2019/2717454