{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T16:58:03Z","timestamp":1772902683189,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T00:00:00Z","timestamp":1662422400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research","award":["SEEIT 03\/2020"],"award-info":[{"award-number":["SEEIT 03\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Computer-aided diagnosis (CAD) systems can be used to process breast ultrasound (BUS) images with the goal of enhancing the capability of diagnosing breast cancer. Many CAD systems operate by analyzing the region-of-interest (ROI) that contains the tumor in the BUS image using conventional texture-based classification models and deep learning-based classification models. Hence, the development of these systems requires automatic methods to localize the ROI that contains the tumor in the BUS image. Deep learning object-detection models can be used to localize the ROI that contains the tumor, but the ROI generated by one model might be better than the ROIs generated by other models. In this study, a new method, called the edge-based selection method, is proposed to analyze the ROIs generated by different deep learning object-detection models with the goal of selecting the ROI that improves the localization of the tumor region. The proposed method employs edge maps computed for BUS images using the recently introduced Dense Extreme Inception Network (DexiNed) deep learning edge-detection model. To the best of our knowledge, our study is the first study that has employed a deep learning edge-detection model to detect the tumor edges in BUS images. The proposed edge-based selection method is applied to analyze the ROIs generated by four deep learning object-detection models. The performance of the proposed edge-based selection method and the four deep learning object-detection models is evaluated using two BUS image datasets. The first dataset, which is used to perform cross-validation evaluation analysis, is a private dataset that includes 380 BUS images. The second dataset, which is used to perform generalization evaluation analysis, is a public dataset that includes 630 BUS images. For both the cross-validation evaluation analysis and the generalization evaluation analysis, the proposed method obtained the overall ROI detection rate, mean precision, mean recall, and mean F1-score values of 98%, 0.91, 0.90, and 0.90, respectively. Moreover, the results show that the proposed edge-based selection method outperformed the four deep learning object-detection models as well as three baseline-combining methods that can be used to combine the ROIs generated by the four deep learning object-detection models. These findings suggest the potential of employing our proposed method to analyze the ROIs generated using different deep learning object-detection models to select the ROI that improves the localization of the tumor region.<\/jats:p>","DOI":"10.3390\/s22186721","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T04:18:32Z","timestamp":1662610712000},"page":"6721","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["An Edge-Based Selection Method for Improving Regions-of-Interest Localizations Obtained Using Multiple Deep Learning Object-Detection Models in Breast Ultrasound Images"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9399-5769","authenticated-orcid":false,"given":"Mohammad I.","family":"Daoud","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, German Jordanian University, Amman-Madaba Street, Amman 11180, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3457-8583","authenticated-orcid":false,"given":"Aamer","family":"Al-Ali","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, German Jordanian University, Amman-Madaba Street, Amman 11180, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1296-0231","authenticated-orcid":false,"given":"Rami","family":"Alazrai","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, German Jordanian University, Amman-Madaba Street, Amman 11180, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8661-5473","authenticated-orcid":false,"given":"Mahasen S.","family":"Al-Najar","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, The University of Jordan Hospital, Queen Rania Al-Abdullah Street, Amman 11942, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4314-2071","authenticated-orcid":false,"given":"Baha A.","family":"Alsaify","sequence":"additional","affiliation":[{"name":"Department of Network Engineering and Security, Jordan University of Science & Technology, Irbid 22110, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3030-848X","authenticated-orcid":false,"given":"Mostafa Z.","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Computer Information Systems, Jordan University of Science & Technology, Irbid 22110, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9321-4005","authenticated-orcid":false,"given":"Sahel","family":"Alouneh","sequence":"additional","affiliation":[{"name":"Cybersecurity Program, College of Engineering, Al Ain University, 28th Street, Abu Dhabi, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,6]]},"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","doi-asserted-by":"crossref","first-page":"87","DOI":"10.3389\/fpubh.2014.00087","article-title":"Delay in breast cancer: Implications for stage at diagnosis and survival","volume":"2","author":"Caplan","year":"2014","journal-title":"Front. Public Health"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1002\/ijc.34038","article-title":"Mammographic breast density, body mass index and risk of breast cancer in Korean women aged 75 years and older","volume":"151","author":"Tran","year":"2022","journal-title":"Int. J. Cancer"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1177\/08465371211011707","article-title":"The added value of supplemental breast ultrasound screening for women with dense breasts: A single center Canadian experience","volume":"73","author":"Wu","year":"2021","journal-title":"Can. Assoc. Radiol. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1148\/radiol.2019182947","article-title":"Breast MRI: State of the art","volume":"292","author":"Mann","year":"2019","journal-title":"Radiology"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mao, Y.J., Lim, H.J., Ni, M., Yan, W.H., Wong, D.W.C., and Cheung, J.C.W. (2022). Breast tumour classification using ultrasound elastography with machine learning: A systematic scoping review. Cancers, 14.","DOI":"10.3390\/cancers14020367"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"AlSawaftah, N., El-Abed, S., Dhou, S., and Zakaria, A. (2022). Microwave imaging for early breast cancer detection: Current state, challenges, and future directions. J. Imaging, 8.","DOI":"10.3390\/jimaging8050123"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1148\/radiol.2333031484","article-title":"Diagnostic accuracy of mammography, clinical examination, US, and MR imaging in preoperative assessment of breast cancer","volume":"233","author":"Berg","year":"2004","journal-title":"Radiology"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.ajog.2014.06.048","article-title":"Screening ultrasound as an adjunct to mammography in women with mammographically dense breasts","volume":"212","author":"Scheel","year":"2015","journal-title":"Am. J. Obstet. Gynecol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.ejca.2018.08.029","article-title":"A prospective comparative trial of adjunct screening with tomosynthesis or ultrasound in women with mammography-negative dense breasts (ASTOUND-2)","volume":"104","author":"Tagliafico","year":"2018","journal-title":"Eur. J. Cancer"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1093\/jbi\/wbab022","article-title":"Artificial intelligence for breast ultrasound: Will it impact radiologists\u2019 accuracy?","volume":"3","author":"Bahl","year":"2021","journal-title":"J. Breast Imaging"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"115204","DOI":"10.1016\/j.eswa.2021.115204","article-title":"A review on image-based approaches for breast cancer detection, segmentation, and classification","volume":"182","author":"Rezaei","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.eng.2018.11.020","article-title":"Deep learning in medical ultrasound analysis: A review","volume":"5","author":"Liu","year":"2019","journal-title":"Engineering"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3024","DOI":"10.1118\/1.4921123","article-title":"Computer-aided diagnosis for distinguishing between triple-negative breast cancer and fibroadenomas based on ultrasound texture features","volume":"42","author":"Moon","year":"2015","journal-title":"Med. Phys."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1186\/s12938-015-0022-8","article-title":"Robust phase-based texture descriptor for classification of breast ultrasound images","volume":"14","author":"Cai","year":"2015","journal-title":"Biomed. Eng. Online"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4112","DOI":"10.1002\/mp.13082","article-title":"Classification of breast lesions in ultrasonography using sparse logistic regression and morphology-based texture features","volume":"45","author":"Nemat","year":"2018","journal-title":"Med. Phys."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1002\/mp.13361","article-title":"Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion","volume":"46","author":"Byra","year":"2019","journal-title":"Med. Phys."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Cao, Z., Duan, L., Yang, G., Yue, T., and Chen, Q. (2019). An experimental study on breast lesion detection and classification from ultrasound images using deep learning architectures. BMC Med. Imaging, 19.","DOI":"10.1186\/s12880-019-0349-x"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.patcog.2014.07.026","article-title":"Fully automatic segmentation of breast ultrasound images based on breast characteristics in space and frequency domains","volume":"48","author":"Xian","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.ultrasmedbio.2011.10.022","article-title":"Completely automated segmentation approach for breast ultrasound images using multiple-domain features","volume":"38","author":"Shan","year":"2012","journal-title":"Ultrasound Med. Biol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.patcog.2009.06.002","article-title":"Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis of ultrasound images","volume":"43","author":"Liu","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 8\u201316). SSD: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 11\u201318). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Las Condes, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_26","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv."},{"key":"ref_27","unstructured":"Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., and Tian, Q. (November, January 27). CenterNet: Keypoint triplets for object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liu, G., Wu, J., Yang, H., Li, Y., Sun, X., Tan, J., and Luo, B. (2022, June 06). Breast Ultrasound Tumor Detection Based on Active Learning and Deep Learning. Available online: www.easychair.org\/publications\/preprint_download\/8WGV.","DOI":"10.1007\/978-981-19-7946-0_1"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). EfficientDet: Scalable and efficient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"101880","DOI":"10.1016\/j.artmed.2020.101880","article-title":"Breast ultrasound region of interest detection and lesion localization","volume":"107","author":"Yap","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Soria, X., Riba, E., and Sappa, A. (2020, January 1\u20135). Dense extreme inception network: Towards a robust CNN model for edge detection. Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Snowmass Village, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093290"},{"key":"ref_32","first-page":"532","article-title":"An analysis of histogram-based thresholding algorithms","volume":"55","author":"Glasbey","year":"1993","journal-title":"CVGIP: Graph. Model. Image Process."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., and Zitnick, C.L. (2014, January 6\u201312). Microsoft COCO: Common objects in context. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"9945","DOI":"10.1109\/ACCESS.2019.2891123","article-title":"Insulator detection in aerial mages for transmission line inspection using single shot multibox detector","volume":"7","author":"Miao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_37","unstructured":"Tan, M., and Le, Q. (2019, January 10\u201315). EfficientNet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"104863","DOI":"10.1016\/j.dib.2019.104863","article-title":"Dataset of breast ultrasound images","volume":"28","author":"Gomaa","year":"2020","journal-title":"Data Brief."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Bottou, L. (2010, January 22\u201327). Large-scale machine learning with stochastic gradient descent. Proceedings of the International Conference on Computational Statistics, Paris, France.","DOI":"10.1007\/978-3-7908-2604-3_16"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/18\/6721\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:24:04Z","timestamp":1760142244000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/18\/6721"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,6]]},"references-count":39,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["s22186721"],"URL":"https:\/\/doi.org\/10.3390\/s22186721","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,6]]}}}