{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T09:44:47Z","timestamp":1750326287574,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030991968"},{"type":"electronic","value":"9783030991975"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-99197-5_10","type":"book-chapter","created":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T09:04:22Z","timestamp":1647939862000},"page":"113-124","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Pulp Stone Detection Using Deep Learning Techniques"],"prefix":"10.1007","author":[{"given":"Amal","family":"Selmi","sequence":"first","affiliation":[]},{"given":"Liyakathunisa","family":"Syed","sequence":"additional","affiliation":[]},{"given":"Bashaer","family":"Abdulkareem","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,23]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Karthick, K., Premkumar, M., Manikandan, R., Cristin, R.: Survey of image processing based applications in AMR. Rev. Comput. Eng. Res. 5(1), 12\u201319 (2018)","DOI":"10.18488\/journal.76.2018.51.12.19"},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal, 42, 60\u201388 (2017)","DOI":"10.1016\/j.media.2017.07.005"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Schwendicke, F., Golla, T., Dreher, M., Krois, J.: Convolutional neural networks for dental image diagnostics: a scoping review. J. Dent. 91, 103226 (2019)","DOI":"10.1016\/j.jdent.2019.103226"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Soltanimehr, E., Bahrampour, E., Imani, M., Rahimi, F., Almasi, B., Moattari, M.: Effect of virtual versus traditional education on theoretical knowledge and reporting skills of dental students in radiographic interpretation of bony lesions of the jaw. BMC Med. Educ. 19(1) (2019)","DOI":"10.1186\/s12909-019-1649-0"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Ahn, E., Kumar, A., Kim, J., Li, C., Feng, D., Fulham, M.: X-ray image classification using domain transferred convolutional neural networks and local sparse spatial pyramid. In: IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 855\u2013858 (2016)","DOI":"10.1109\/ISBI.2016.7493400"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Privado, M., Villal\u00f3n, J., Mart\u00ednez, C., Ivorra, C.: Dental images recognition technology and applications: a literature review. Appl. Sci. 10(8), 2856 (2020)","DOI":"10.3390\/app10082856"},{"key":"10_CR7","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":"10_CR8","doi-asserted-by":"crossref","unstructured":"Muthu Lakshmi, M., Chitra, P.: Classification of dental cavities from X-ray images using deep CNN algorithm. In: 4th International Conference on Trends in Electronics and Informatics (ICOEI) (48184), pp. 774\u2013779 (2020)","DOI":"10.1109\/ICOEI48184.2020.9143013"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Prabhakar, S., Rajaguru, H.: Performance analysis of linear layer neural networks for oral cancer classification. In: 6th ICT International Student Project Conference (ICT-ISPC), pp. 1\u20134 (2017)","DOI":"10.1109\/ICT-ISPC.2017.8075357"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Jader, G., Fontinele, J., Ruiz, M., Abdalla, K., Pithon, M., Oliveira, L.: Deep instance segmentation of teeth in panoramic X-ray images. In: 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), IEEE, Parana (2018)","DOI":"10.1109\/SIBGRAPI.2018.00058"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Lee, J.-H., Kim, D.-H., Jeong, S.-N., Choi, S.-H.: Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J. Dent. 77, 106\u2013111 (2018)","DOI":"10.1016\/j.jdent.2018.07.015"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Sukegawa, S., et al.: Deep neural networks for dental implant system classification. Biomolecules 10 (2020)","DOI":"10.3390\/biom10070984"},{"key":"10_CR13","unstructured":"Galav, A., Vyas, T., Kaur, M., Chauhan, M., Satija, N.: Association of pulp stones & renal stones- a clinical study. J. Oral. Biol. Craniofac. Res. 5(3), 189\u2013192 (2018)"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Memon, M., Kalhoro, F.A., Shams, S., Arain, S.: A study on radiographic assessment of pulp stone. J. Endod. 25, 992\u2013996 (2018)","DOI":"10.29309\/TPMJ\/18.3756"},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Khan, S., Yong, S.-P.: A Deep learning architecture for classifying medical images of anatomy object. In: 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, Kuala Lumpur (2017)","DOI":"10.1109\/APSIPA.2017.8282299"},{"key":"10_CR16","unstructured":"oktay, A.B.: Tooth detection with convolutional neural networks. BioMed. Res. Int. 2021 (2017)"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"10_CR18","unstructured":"Xia, X., Xu, C., Nan, B.: Inception-v3 for flower classification. In: 2017 2nd International Conference on Image, Vision and Computing (ICIVC) (2017)"},{"key":"10_CR19","unstructured":"Papers with code - Inception-v3 explained: Paperswithcode.com. https:\/\/paperswithcode.com\/method\/inception-v3. Accessed: 12 Dec 2020"},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Almabdy, S., Elrefaei, L.: Feature extraction and fusion for face recognition systems using pre-trained convolutional neural networks. Int. J. Comput. Digit. Syst.\u00a09 (2020)","DOI":"10.12785\/ijcds\/100144"},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Rezende, E., Ruppert, G., Carvalho, T., Ramos, F., De Geus, P.: Malicious software classification using transfer learning of resnet-50 deep neural network. In: 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA),\u00a0pp. 1011\u20131014. IEEE (2017)","DOI":"10.1109\/ICMLA.2017.00-19"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Meghana, A.S., Sudhakar, S., Arumugam, G., Srinivasan, P., Prakash, K.B.: Age and gender prediction using convolution, ResNet50 and inception ResNetV2. Int. J. Adv. Trends Comput. Sci. Eng. 9 (2020)","DOI":"10.30534\/ijatcse\/2020\/65922020"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","IoT Technologies for Health Care"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-99197-5_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T09:21:33Z","timestamp":1647940893000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-99197-5_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030991968","9783030991975"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-99197-5_10","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"23 March 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HealthyIoT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"EAI International Conference on IoT Technologies for HealthCare","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"healthyiot2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/healthyiot.eai-conferences.org\/2021","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Confy+","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"40","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":"17","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":"0","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":"43% - 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":"3","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":"3","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)"}}]}}