{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T06:09:19Z","timestamp":1748585359683,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031314063"},{"type":"electronic","value":"9783031314070"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-31407-0_39","type":"book-chapter","created":{"date-parts":[[2023,5,6]],"date-time":"2023-05-06T12:02:31Z","timestamp":1683374551000},"page":"524-537","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An Efficient Deep Transfer Learning Approach for\u00a0Classification of\u00a0Skin Cancer Images"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8599-183X","authenticated-orcid":false,"given":"Prithviraj Purushottam","family":"Naik","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4049-3677","authenticated-orcid":false,"given":"B.","family":"Annappa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1001-1187","authenticated-orcid":false,"given":"Shubham","family":"Dodia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,7]]},"reference":[{"issue":"2","key":"39_CR1","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/j.amepre.2014.08.036","volume":"48","author":"GP Guy Jr","year":"2015","unstructured":"Guy, G.P., Jr., Machlin, S.R., Ekwueme, D.U., Yabroff, K.R.: Prevalence and costs of skin cancer treatment in the US, 2002\u20132006 and 2007\u20132011. Am. J. Prev. Med. 48(2), 183\u2013187 (2015)","journal-title":"Am. J. Prev. Med."},{"key":"39_CR2","unstructured":"\u201cInfographic: Don\u2019t Fry: Preventing Skin Cancer\u201d. American Cancer Society. Accessed 10 May 2022. https:\/\/www.cancer.org\/healthy\/be-safe-in-sun\/skin-cancer-prevention-infographic.html"},{"key":"39_CR3","unstructured":"Cancer Facts & Figures 2022: American Cancer Society. Accessed 10 May 2022. https:\/\/www.cancer.org\/research\/cancer-facts-statistics\/all-cancer-facts-figures\/cancer-facts-figures-2022.html"},{"key":"39_CR4","doi-asserted-by":"crossref","unstructured":"Hill, L., Ferrini, R.L.: Skin cancer prevention and screening: summary of the American College of Preventive Medicine\u2019s practice policy statements. CA Cancer J. Clin. 48(4), 232\u2013235 (1998)","DOI":"10.3322\/canjclin.48.4.232"},{"issue":"10","key":"39_CR5","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2009)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"39_CR6","doi-asserted-by":"crossref","unstructured":"Tschandl, P., Rosendahl, C., Kittler, H.: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5(1), 1\u20139 (2018)","DOI":"10.1038\/sdata.2018.161"},{"key":"39_CR7","doi-asserted-by":"crossref","unstructured":"Hassan, S.R., Afroge, S., Mizan, M.B.: Skin lesion classification using densely connected convolutional network. In: 2020 IEEE Region 10 Symposium (TENSYMP), pp. 750\u2013753, IEEE (2020)","DOI":"10.1109\/TENSYMP50017.2020.9231041"},{"key":"39_CR8","doi-asserted-by":"crossref","unstructured":"Kondaveeti, H.K., Edupuganti, P.: Skin Cancer Classification using Transfer Learning. In: 2020 IEEE International Conference on Advent Trends in Multidisciplinary Research and Innovation (ICATMRI), pp. 1\u20134. IEEE(2020)","DOI":"10.1109\/ICATMRI51801.2020.9398388"},{"key":"39_CR9","doi-asserted-by":"crossref","unstructured":"Rahman, Z., Ami, A.M.: A transfer learning based approach for skin lesion classification from imbalanced data. In: 2020 11th International Conference on Electrical and Computer Engineering (ICECE), pp. 65\u201368. IEEE (2020)","DOI":"10.1109\/ICECE51571.2020.9393155"},{"key":"39_CR10","doi-asserted-by":"crossref","unstructured":"Subramanian, R.R., Achuth, D., Kumar, P.S., kumar Reddy, K.N., Amara, S., Chowdary, A.S.: Skin cancer classification using Convolutional neural networks. In: 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp. 13\u201319. IEEE (2021)","DOI":"10.1109\/Confluence51648.2021.9377155"},{"issue":"23","key":"39_CR11","doi-asserted-by":"publisher","first-page":"8142","DOI":"10.3390\/s21238142","volume":"21","author":"S Jain","year":"2021","unstructured":"Jain, S., Singhania, U., Tripathy, B., Nasr, E.A., Aboudaif, M.K., Kamrani, A.K.: Deep learning-based transfer learning for classification of skin cancer. Sensors 21(23), 8142 (2021)","journal-title":"Sensors"},{"key":"39_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1007\/978-3-030-01424-7_27","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2018","author":"C Tan","year":"2018","unstructured":"Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., Liu, C.: A Survey on Deep Transfer Learning. In: K\u016frkov\u00e1, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11141, pp. 270\u2013279. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01424-7_27"},{"key":"39_CR13","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":"39_CR14","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"39_CR15","unstructured":"Tan, M., Le, Q.: Efficientnet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning. pp. 6105\u20136114. PMLR, (2019)"},{"key":"39_CR16","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"39_CR17","unstructured":"Tan, M., Le, Q.: EfficientNetV2: smaller models and faster training. In: International Conference on Machine Learning. pp. 10096\u201310106. PMLR (2021)"},{"issue":"19","key":"39_CR18","doi-asserted-by":"publisher","first-page":"6940","DOI":"10.3390\/app10196940","volume":"10","author":"V Taormina","year":"2020","unstructured":"Taormina, V., Cascio, D., Abbene, L., Raso, G.: Performance of fine-tuning convolutional neural networks for HEP-2 image classification. Appl. Sci. 10(19), 6940 (2020)","journal-title":"Appl. Sci."},{"key":"39_CR19","doi-asserted-by":"crossref","unstructured":"Deng, J., et al:. ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"39_CR20","doi-asserted-by":"crossref","unstructured":"Huo, Y.: Full-stack application of skin cancer diagnosis based on CNN Model. In: 2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI). pp. 754\u2013758. IEEE (2021)","DOI":"10.1109\/CEI52496.2021.9574583"}],"container-title":["Communications in Computer and Information Science","Computer Vision and Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-31407-0_39","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,19]],"date-time":"2024-10-19T22:46:05Z","timestamp":1729377965000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-31407-0_39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031314063","9783031314070"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-31407-0_39","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"7 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CVIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Vision and Image Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nagpur","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cvip2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/vnit.ac.in\/cvip2022\/","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"307","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":"110","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":"11","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":"36% - 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":"4","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)"}}]}}