{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T08:23:19Z","timestamp":1746260599610,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031346187"},{"type":"electronic","value":"9783031346194"}],"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\/978-3-031-34619-4_19","type":"book-chapter","created":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T19:01:31Z","timestamp":1686423691000},"page":"226-237","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Cancer Diseases Diagnosis Using Deep Transfer Learning Architectures"],"prefix":"10.1007","author":[{"given":"Tania Ferdousey","family":"Promy","sequence":"first","affiliation":[]},{"given":"Nadia Islam","family":"Joya","sequence":"additional","affiliation":[]},{"given":"Tasfia Haque","family":"Turna","sequence":"additional","affiliation":[]},{"given":"Zinia Nawrin","family":"Sukhi","sequence":"additional","affiliation":[]},{"given":"Faisal Bin","family":"Ashraf","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3403-4095","authenticated-orcid":false,"given":"Jia","family":"Uddin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,11]]},"reference":[{"key":"19_CR1","doi-asserted-by":"crossref","unstructured":"Bhuiyan, M.R., et al.: A deep crowd density classification model for Hajj pilgrimage using fully convolutional neural network. PeerJ Comput. Sci. 25(8), e895 (2022)","DOI":"10.7717\/peerj-cs.895"},{"key":"19_CR2","series-title":"Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1007\/978-3-030-60036-5_11","volume-title":"Emerging Technologies in Computing","author":"MN Sabab","year":"2020","unstructured":"Sabab, M.N., Chowdhury, M.A.R., Nirjhor, S.M.M.I., Uddin, J.: Bangla speech recognition using 1D-CNN and LSTM with different dimension reduction techniques. In: Miraz, M.H., Excell, P.S., Ware, A., Soomro, S., Ali, M. (eds.) iCETiC 2020. LNICSSITE, vol. 332, pp. 158\u2013169. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-60036-5_11"},{"key":"19_CR3","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25 (2012)"},{"key":"19_CR4","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: The International Conference on Machine Learning, pp. 448\u2013456. PMLR (2015)"},{"issue":"4","key":"19_CR5","doi-asserted-by":"publisher","first-page":"37","DOI":"10.33166\/AETiC.2021.04.004","volume":"5","author":"ZM Ruhi","year":"2021","unstructured":"Ruhi, Z.M., Jahan, S., Uddin, J.: A novel hybrid signal decomposition technique for transfer learning based industrial fault diagnosis. Ann. Emerg. Technol. Comput. 5(4), 37\u201353 (2021). https:\/\/doi.org\/10.33166\/AETiC.2021.04.004","journal-title":"Ann. Emerg. Technol. Comput."},{"key":"19_CR6","unstructured":"Brownlee, J.: A gentle introduction to transfer learning for deep learning. Mach. Learn. Mastery 20 (2017)"},{"key":"19_CR7","unstructured":"CDCBreastCancer. \u201cWhat is a mammogram?\u201d Centers for Disease Control and Prevention (2022). https:\/\/www.cdc.gov\/cancer\/breast\/basicinfo\/mammograms.htm. Accessed 12 May 2022"},{"key":"19_CR8","doi-asserted-by":"crossref","unstructured":"Guan, S., Loew, M.: Breast cancer detection using transfer learning in convolutional neural networks. In: 2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp. 1\u20138. IEEE (2017)","DOI":"10.1109\/AIPR.2017.8457948"},{"key":"19_CR9","unstructured":"Suckling, J.P.: The mammographic image analysis society digital mammogram database. Digit. Mammo 375\u2013386 (1994)"},{"key":"19_CR10","unstructured":"Pub, M.H., Bowyer, K., Kopans, D., Moore, R., Kegelmeyer, P.: The digital database for screening mammography. In: Fifth International Workshop on Digital Mammography, pp. 212\u2013218 (2001)"},{"issue":"11","key":"19_CR11","doi-asserted-by":"publisher","first-page":"111","DOI":"10.3390\/data6110111","volume":"6","author":"AS Alsolami","year":"2021","unstructured":"Alsolami, A.S., Shalash, W., Alsaggaf, W., Ashoor, S., Refaat, H., Elmogy, M.: King Abdulaziz University breast cancer mammogram dataset (KAU-BCMD). Data 6(11), 111 (2021)","journal-title":"Data"},{"key":"19_CR12","doi-asserted-by":"publisher","first-page":"e638","DOI":"10.7717\/peerj-cs.638","volume":"7","author":"MN Islam","year":"2021","unstructured":"Islam, M.N., et al.: Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline. PeerJ Comput. Sci. 7, e638 (2021)","journal-title":"PeerJ Comput. Sci."},{"key":"19_CR13","doi-asserted-by":"publisher","first-page":"e6201","DOI":"10.7717\/peerj.6201","volume":"7","author":"DA Ragab","year":"2019","unstructured":"Ragab, D.A., Sharkas, M., Marshall, S., Ren, J.: Breast cancer detection using deep convolutional neural networks and support vector machines. PeerJ 7, e6201 (2019)","journal-title":"PeerJ"},{"key":"19_CR14","doi-asserted-by":"crossref","unstructured":"Fedorov, A., et al.: Standardized representation of the LIDC annotations using DICOM (No. e27378v2). PeerJ Preprints (2019)","DOI":"10.7287\/peerj.preprints.27378v2"},{"key":"19_CR15","doi-asserted-by":"crossref","unstructured":"Pehrson, L.M., Nielsen, M.B., Ammitzb\u00f8l Lauridsen, C.: Automatic pulmonary nodule detection applying deep learning or machine learning algorithms to the LIDC-IDRI database: a systematic review. Diagnostics 9(1), 29 (2019)","DOI":"10.3390\/diagnostics9010029"},{"issue":"4","key":"19_CR16","doi-asserted-by":"publisher","first-page":"339","DOI":"10.18280\/ts.360406","volume":"36","author":"T Sajja","year":"2019","unstructured":"Sajja, T., Devarapalli, R., Kalluri, H.: Lung cancer detection based on CT scan images by using deep transfer learning. Traitement du Signal 36(4), 339\u2013344 (2019)","journal-title":"Traitement du Signal"},{"issue":"4","key":"19_CR17","doi-asserted-by":"publisher","first-page":"1227","DOI":"10.1109\/JBHI.2017.2725903","volume":"22","author":"H Jiang","year":"2017","unstructured":"Jiang, H., Ma, H., Qian, W., Gao, M., Li, Y.: An automatic detection system of lung nodules based on a multigroup patch-based deep learning network. IEEE J. Biomed. Health Inform. 22(4), 1227\u20131237 (2017)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"19_CR18","doi-asserted-by":"crossref","unstructured":"Da N\u00f3brega, R.V.M., Peixoto, S.A., da Silva, S.P.P., Rebou\u00e7as Filho, P.P.: Lung nodule classification via deep transfer learning in CT lung images. In: 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), pp. 244\u2013249. IEEE (2018)","DOI":"10.1109\/CBMS.2018.00050"},{"key":"19_CR19","doi-asserted-by":"crossref","unstructured":"Lyu, J., Ling, S.H.: Using multi-level convolutional neural networks for classification of lung nodules on CT images. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 686\u2013689. IEEE (2018)","DOI":"10.1109\/EMBC.2018.8512376"},{"key":"19_CR20","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 (ICATMI), pp. 1\u20134. IEEE (2020)","DOI":"10.1109\/ICATMRI51801.2020.9398388"},{"issue":"1","key":"19_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.161","volume":"5","author":"P Tschandl","year":"2018","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)","journal-title":"Sci. Data"},{"key":"19_CR22","doi-asserted-by":"crossref","unstructured":"Lekamlage, C.D., Afzal, F., Westerberg, E., Cheddad, A.: Mini-DDSM: mammography-based automatic age estimation. In: 2020 3rd International Conference on Digital Medicine and Image Processing, pp. 1\u20136 (2020)","DOI":"10.1145\/3441369.3441370"},{"key":"19_CR23","doi-asserted-by":"crossref","unstructured":"Kareem, H.F., AL-Husieny, M.S., Mohsen, F.Y., Khalil, E.A., Hassan, Z.S.: Evaluation of SVM performance in the detection of lung cancer in marked CT scan dataset. Indonesian J. Electr. Eng. Comput. Sci. 21(3), 1731\u20131738 (2021)","DOI":"10.11591\/ijeecs.v21.i3.pp1731-1738"},{"key":"19_CR24","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.patrec.2019.11.013","volume":"129","author":"A Bhandary","year":"2020","unstructured":"Bhandary, A., et al.: Deep-learning framework to detect lung abnormality\u2013a study with chest X-Ray and lung CT scan images. Pattern Recogn. Lett. 129, 271\u2013278 (2020)","journal-title":"Pattern Recogn. Lett."},{"key":"19_CR25","unstructured":"Sachan, A.N.K.I.T.: Detailed guide to understand and implement ResNets (2019). Accessed 5 Nov 2020"},{"key":"19_CR26","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetv2: inverted residuals and linear bottlenecks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Machine Intelligence and Emerging Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-34619-4_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T19:06:57Z","timestamp":1686424017000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-34619-4_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031346187","9783031346194"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-34619-4_19","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"11 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIET","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Intelligence and Emerging Technologies","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Noakhali","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bangladesh","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":"23 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miet2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/confmiet.org","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 plus","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"272","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":"104","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":"38% - 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","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":"2","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)"}}]}}