{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T16:10:22Z","timestamp":1781194222629,"version":"3.54.1"},"reference-count":28,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,3]],"date-time":"2022-02-03T00:00:00Z","timestamp":1643846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Economic Affairs, Taiwan","award":["109-EC-17-A-02-S5-008"],"award-info":[{"award-number":["109-EC-17-A-02-S5-008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Globally, the incidence rate for breast cancer ranks first. Treatment for early-stage breast cancer is highly cost effective. Five-year survival rate for stage 0\u20132 breast cancer exceeds 90%. Screening mammography has been acknowledged as the most reliable way to diagnose breast cancer at an early stage. Taiwan government has been urging women without any symptoms, aged between 45 and 69, to have a screening mammogram bi-yearly. This brings about a large workload for radiologists. In light of this, this paper presents a deep neural network (DNN)-based model as an efficient and reliable tool to assist radiologists with mammographic interpretation. For the first time in the literature, mammograms are completely classified into BI-RADS categories 0, 1, 2, 3, 4A, 4B, 4C and 5. The proposed model was trained using block-based images segmented from a mammogram dataset of our own. A block-based image was applied to the model as an input, and a BI-RADS category was predicted as an output. At the end of this paper, the outperformance of this work is demonstrated by an overall accuracy of 94.22%, an average sensitivity of 95.31%, an average specificity of 99.15% and an area under curve (AUC) of 0.9723. When applied to breast cancer screening for Asian women who are more likely to have dense breasts, this model is expected to give a higher accuracy than others in the literature, since it was trained using mammograms taken from Taiwanese women.<\/jats:p>","DOI":"10.3390\/s22031160","type":"journal-article","created":{"date-parts":[[2022,2,6]],"date-time":"2022-02-06T20:40:18Z","timestamp":1644180018000},"page":"1160","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["A High-Performance Deep Neural Network Model for BI-RADS Classification of Screening Mammography"],"prefix":"10.3390","volume":"22","author":[{"given":"Kuen-Jang","family":"Tsai","sequence":"first","affiliation":[{"name":"Department of General Surgey, E-Da Cancer Hospital, Yanchao Dist., Kaohsiung 82445, Taiwan"},{"name":"College of Medicine, I-Shou University, Yanchao Dist., Kaohsiung 82445, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mei-Chun","family":"Chou","sequence":"additional","affiliation":[{"name":"Department of Radiology, E-Da Hospital, Yanchao Dist., Kaohsiung 82445, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7815-6484","authenticated-orcid":false,"given":"Hao-Ming","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiology, E-Da Hospital, Yanchao Dist., Kaohsiung 82445, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shin-Tso","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology, E-Da Hospital, Yanchao Dist., Kaohsiung 82445, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jung-Hsiu","family":"Hsu","sequence":"additional","affiliation":[{"name":"Department of Radiology, E-Da Hospital, Yanchao Dist., Kaohsiung 82445, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei-Cheng","family":"Yeh","sequence":"additional","affiliation":[{"name":"Department of Radiology, E-Da Cancer Hospital, Yanchao Dist., Kaohsiung 82445, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao-Ming","family":"Hung","sequence":"additional","affiliation":[{"name":"Department of General Surgey, E-Da Cancer Hospital, Yanchao Dist., Kaohsiung 82445, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4227-3571","authenticated-orcid":false,"given":"Cheng-Yu","family":"Yeh","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaw-Hwa","family":"Hwang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA-Cancer J. Clin."},{"key":"ref_2","unstructured":"(2021, October 06). Cancer Registry Annual Report, 2018 Taiwan. Health Promotion Administration, Ministry of Health and Welfare, Taiwan, December 2020, Available online: https:\/\/www.hpa.gov.tw\/EngPages\/Detail.aspx?nodeid=1061&pid=6069."},{"key":"ref_3","first-page":"376","article-title":"Breast cancer trend in Taiwan","volume":"6","author":"Chen","year":"2017","journal-title":"MOJ Women\u2019s Health"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1148\/rg.2016150178","article-title":"A Pictorial Review of Changes in the BI-RADS Fifth Edition","volume":"36","author":"Rao","year":"2016","journal-title":"Radiographics"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/s41746-021-00438-z","article-title":"Diagnostic accuracy of deep learning in medical imaging: A systematic review and meta-analysis","volume":"4","author":"Aggarwal","year":"2021","journal-title":"NPJ Digit. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"104248","DOI":"10.1016\/j.compbiomed.2021.104248","article-title":"Convolutional neural networks for breast cancer detection in mammography: A survey","volume":"131","author":"Abdelrahman","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"114161","DOI":"10.1016\/j.eswa.2020.114161","article-title":"Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review","volume":"167","author":"Houssein","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5902","DOI":"10.1007\/s00330-020-07659-y","article-title":"A deep learning model integrating mammography and clinical factors facilitates the malignancy prediction of BI-RADS 4 microcalcifications in breast cancer screening","volume":"31","author":"Liu","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"27327","DOI":"10.1038\/srep27327","article-title":"Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning","volume":"6","author":"Wang","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"109309","DOI":"10.1016\/j.ejrad.2020.109309","article-title":"Combined texture analysis and machine learning in suspicious calcifications detected by mammography: Potential to avoid unnecessary stereotactical biopsies","volume":"132","author":"Stelzer","year":"2020","journal-title":"Eur. J. Radiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"102204","DOI":"10.1016\/j.media.2021.102204","article-title":"MommiNet-v2: Mammographic multi-view mass identification networks","volume":"73","author":"Yang","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_12","first-page":"012703","article-title":"Evaluation of data augmentation via synthetic images for improved breast mass detection on mammograms using deep learning","volume":"7","author":"Cha","year":"2020","journal-title":"J. Med. Imaging"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"7695207","DOI":"10.1155\/2020\/7695207","article-title":"A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms","volume":"2020","author":"Boumaraf","year":"2020","journal-title":"BioMed Res. Int."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1080\/21681163.2017.1350206","article-title":"A CNN based method for automatic mass detection and classification in mammograms","volume":"7","author":"Karlinsky","year":"2019","journal-title":"Comput. Methods Biomech. Biomed. Eng. Imaging Vis."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1184","DOI":"10.1109\/TMI.2019.2945514","article-title":"Deep Neural Networks Improve Radiologists Performance in Breast Cancer Screening","volume":"39","author":"Wu","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1007\/s12282-020-01061-8","article-title":"Artificial intelligence for breast cancer detection in mammography: Experience of use of the ScreenPoint Medical Transpara system in 310 Japanese women","volume":"27","author":"Sasaki","year":"2020","journal-title":"Breast Cancer"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1007\/s10916-019-1494-z","article-title":"Classification of Mammogram Images Using Multiscale all Convolutional Neural Network (MA-CNN)","volume":"44","author":"Agnes","year":"2020","journal-title":"J. Med. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4165","DOI":"10.1038\/s41598-018-22437-z","article-title":"Detecting and classifying lesions in mammograms with Deep Learning","volume":"8","author":"Ribli","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"926","DOI":"10.1109\/TLA.2018.8358675","article-title":"Detection and Classification of Lesions in Mammographies Using Neural Networks and Morphological Wavelets","volume":"16","author":"Cruz","year":"2018","journal-title":"IEEE Lat. Am. Trans."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2355","DOI":"10.1109\/TMI.2017.2751523","article-title":"Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning","volume":"36","author":"Carneiro","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"12495","DOI":"10.1038\/s41598-019-48995-4","article-title":"Deep learning to improve breast cancer detection on screening mammography","volume":"9","author":"Shen","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_22","first-page":"10","article-title":"Five Classifications of Mammography Images Based on Deep Cooperation Convolutional Neural Network","volume":"57","author":"Tang","year":"2019","journal-title":"Am. Sci. Res. J. Eng. Technol. Sci."},{"key":"ref_23","unstructured":"American College of Radiology (ACR) (2013). ACR BI-RADS Atlas, ACR. [5th ed.]."},{"key":"ref_24","unstructured":"Tan, M., and Le, Q.V. (2020). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2019). MobileNetV2: Inverted Residuals and Linear Bottlenecks. arXiv.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_26","unstructured":"Ramachandran, P., Zoph, B., and Le, Q.V. (2017). Searching for Activation Functions. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-Excitation Networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","unstructured":"Wright, L., and Demeure, N. (2021). Ranger21: A synergistic deep learning optimizer. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/1160\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:13:25Z","timestamp":1760134405000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/1160"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,3]]},"references-count":28,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["s22031160"],"URL":"https:\/\/doi.org\/10.3390\/s22031160","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,3]]}}}