{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T08:47:23Z","timestamp":1743065243506,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030656201"},{"type":"electronic","value":"9783030656218"}],"license":[{"start":{"date-parts":[[2020,12,12]],"date-time":"2020-12-12T00:00:00Z","timestamp":1607731200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,12,12]],"date-time":"2020-12-12T00:00:00Z","timestamp":1607731200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-65621-8_16","type":"book-chapter","created":{"date-parts":[[2020,12,11]],"date-time":"2020-12-11T12:32:07Z","timestamp":1607689927000},"page":"253-267","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Automated Diagnosis of Breast Cancer with RoI Detection Using YOLO and Heuristics"],"prefix":"10.1007","author":[{"given":"Ananya","family":"Bal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meenakshi","family":"Das","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1665-8101","authenticated-orcid":false,"given":"Shashank Mouli","family":"Satapathy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Madhusmita","family":"Jena","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Subha Kanta","family":"Das","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,12,12]]},"reference":[{"key":"16_CR1","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.ijmedinf.2018.06.003","volume":"117","author":"MA Al-Antari","year":"2018","unstructured":"Al-Antari, M.A., Al-Masni, M.A., Choi, M.T., Han, S.M., Kim, T.S.: A fully integrated computer-aided diagnosis system for digital x-ray mammograms via deep learning detection, segmentation, and classification. Int. J. Med. Inf. 117, 44\u201354 (2018)","journal-title":"Int. J. Med. Inf."},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Al-antari, M.A., Kim, T.S.: Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital x-ray mammograms. Computer Methods and Programs in Biomedicine p. 105584 (2020)","DOI":"10.1016\/j.cmpb.2020.105584"},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"Al-masni, M.A., et al.: Detection and classification of the breast abnormalities in digital mammograms via regional convolutional neural network. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). pp. 1230\u20131233. IEEE (2017)","DOI":"10.1109\/EMBC.2017.8037053"},{"key":"16_CR4","unstructured":"Breastcancer.org: Invasive ductal carcinoma: Diagnosis, treatment, and more. https:\/\/www.breastcancer.org\/symptoms\/types\/idc (2019)"},{"key":"16_CR5","unstructured":"Cancer Today: International Agency for research on Cancer: Iarc world cancer report 2020. https:\/\/www.iccp-portal.org\/sites\/default\/files\/resources\/IARC-World-Cancer-Report-2020.pdf (2018). Accessed: 20 Feb 2020"},{"key":"16_CR6","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1016\/j.future.2019.04.031","volume":"100","author":"S Ding","year":"2019","unstructured":"Ding, S., Li, L., Li, Z., Wang, H., Zhang, Y.: Smart electronic gastroscope system using a cloud-edge collaborative framework. Future Generation Comput. Syst. 100, 395\u2013407 (2019)","journal-title":"Future Generation Comput. Syst."},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Gao, X., Braden, B., Taylor, S., Pang, W.: Towards real-time detection of squamous pre-cancers from oesophageal endoscopic videos. In: 2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA). pp. 1606\u20131612. IEEE (2019)","DOI":"10.1109\/ICMLA.2019.00264"},{"key":"16_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"16_CR9","unstructured":"Hinton, G., Srivastava, N., Swersky, K.: Coursera: Neural networks for machine learning: Lecture 6(a)\u2013overview of mini-batch gradient descent. https:\/\/www.cs.toronto.edu\/~tijmen\/csc321\/slides\/lecture_slides_lec6.pdf (2014)"},{"key":"16_CR10","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)"},{"key":"16_CR11","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"16_CR12","unstructured":"Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of ICML. vol. 30, p. 3 (2013)"},{"key":"16_CR13","unstructured":"National Cancer Institute (NCI-AIIMS: Cancer statistics \u2014 drupal. http:\/\/nciindia.aiims.edu\/en\/cancer-statistics (2020)"},{"key":"16_CR14","unstructured":"National Centre for Disease Informatics and Research: NCPR three-year report of population based cancer registries 2012\u20132014. https:\/\/ncdirindia.org\/NCRP\/ALL_NCRP_REPORTS\/PBCR_REPORT_2012_2014\/ALL_CONTENT\/PDF_Printed_Version\/Chapter10_Printed.pdf (2020)"},{"key":"16_CR15","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"16_CR16","unstructured":"Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018)"},{"key":"16_CR17","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.tice.2019.02.001","volume":"57","author":"AR Saikia","year":"2019","unstructured":"Saikia, A.R., Bora, K., Mahanta, L.B., Das, A.K.: Comparative assessment of cnn architectures for classification of breast fnac images. Tissue Cell 57, 8\u201314 (2019)","journal-title":"Tissue Cell"},{"key":"16_CR18","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: Breast cancer histopathological image classification using convolutional neural networks. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 2560\u20132567. IEEE (2016)","DOI":"10.1109\/IJCNN.2016.7727519"},{"key":"16_CR20","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference On Computer Vision And Pattern Recognition. pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"16_CR21","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference On Computer Vision And Pattern Recognition, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"16_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"812","DOI":"10.1007\/978-3-319-93000-8_92","volume-title":"Image Analysis and Recognition","author":"S Vesal","year":"2018","unstructured":"Vesal, S., Ravikumar, N., Davari, A.A., Ellmann, S., Maier, A.: Classification of breast cancer histology images using transfer learning. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds.) ICIAR 2018. LNCS, vol. 10882, pp. 812\u2013819. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-93000-8_92"}],"container-title":["Lecture Notes in Computer Science","Distributed Computing and Internet Technology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-65621-8_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,15]],"date-time":"2020-12-15T00:25:00Z","timestamp":1607991900000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-65621-8_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,12]]},"ISBN":["9783030656201","9783030656218"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-65621-8_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020,12,12]]},"assertion":[{"value":"12 December 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICDCIT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Distributed Computing and Internet Technology","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bhubaneswar","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 January 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 January 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icdcit2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icdcit.ac.in\/17th-icdcit-2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"99","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"13","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"13% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}