{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T15:28:24Z","timestamp":1759332504922,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"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_15","type":"book-chapter","created":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T19:01:31Z","timestamp":1686423691000},"page":"177-186","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Tumor-TL: A Transfer Learning Approach for Classifying Brain Tumors from MRI Images"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1561-960X","authenticated-orcid":false,"given":"Abu Kowshir","family":"Bitto","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4756-3025","authenticated-orcid":false,"given":"Md. Hasan Imam","family":"Bijoy","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5275-9487","authenticated-orcid":false,"given":"Sabina","family":"Yesmin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9383-8739","authenticated-orcid":false,"given":"Md. Jueal","family":"Mia","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,11]]},"reference":[{"issue":"5","key":"15_CR1","doi-asserted-by":"publisher","first-page":"1240","DOI":"10.1109\/TMI.2016.2538465","volume":"35","author":"S Pereira","year":"2016","unstructured":"Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imaging 35(5), 1240\u20131251 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"15_CR2","doi-asserted-by":"publisher","first-page":"103345","DOI":"10.1016\/j.compbiomed.2019.103345","volume":"111","author":"S Deepak","year":"2019","unstructured":"Deepak, S., Ameer, P.M.: Brain tumor classification using deep CNN features via transfer learning. Comput. Biol. Med. 111, 103345 (2019)","journal-title":"Comput. Biol. Med."},{"key":"15_CR3","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.cogsys.2018.12.007","volume":"54","author":"M Talo","year":"2019","unstructured":"Talo, M., Baloglu, U.B., Y\u0131ld\u0131r\u0131m, \u00d6., Rajendra Acharya, U.: Application of deep transfer learning for automated brain abnormality classification using MR images. Cogn. Syst. Res. 54, 176\u2013188 (2019)","journal-title":"Cogn. Syst. Res."},{"key":"15_CR4","doi-asserted-by":"crossref","unstructured":"Ahuja, S., Panigrahi, B.K., Gandhi, T.: Transfer learning based brain tumor detection and segmentation using superpixel technique. In: 2020 International Conference on Contemporary Computing and Applications (IC3A), pp. 244\u2013249. IEEE (2020)","DOI":"10.1109\/IC3A48958.2020.233306"},{"issue":"8","key":"15_CR5","doi-asserted-by":"publisher","first-page":"565","DOI":"10.3390\/diagnostics10080565","volume":"10","author":"MA Khan","year":"2020","unstructured":"Khan, M.A., et al.: Multimodal brain tumor classification using deep learning and robust feature selection: a machine learning application for radiologists. Diagnostics 10(8), 565 (2020). https:\/\/doi.org\/10.3390\/diagnostics10080565","journal-title":"Diagnostics"},{"issue":"3","key":"15_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00138-020-01069-2","volume":"31","author":"T Kaur","year":"2020","unstructured":"Kaur, T., Gandhi, T.K.: Deep convolutional neural networks with transfer learning for automated brain image classification. Mach. Vis. Appl. 31(3), 1\u201316 (2020). https:\/\/doi.org\/10.1007\/s00138-020-01069-2","journal-title":"Mach. Vis. Appl."},{"key":"15_CR7","unstructured":"Nickparvar, M.: Brain tumor MRI dataset. Kaggle (2021). https:\/\/www.kaggle.com\/datasets\/masoudnickparvar\/brain-tumor-mri-dataset. Accessed 24 Mar 2022"},{"key":"15_CR8","doi-asserted-by":"publisher","unstructured":"Mia, J., Bijoy, H.I., Uddin, S., Raza, D.M.: Real-time herb leaves localization and classification using YOLO. In: 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1\u20137 (2021). https:\/\/doi.org\/10.1109\/ICCCNT51525.2021.9579718","DOI":"10.1109\/ICCCNT51525.2021.9579718"},{"issue":"11","key":"15_CR9","doi-asserted-by":"publisher","first-page":"1081","DOI":"10.1109\/TSE.2018.2821670","volume":"45","author":"R Krishna","year":"2018","unstructured":"Krishna, R., Menzies, T.: Bellwethers: a baseline method for transfer learning. IEEE Trans. Softw. Eng. 45(11), 1081\u20131105 (2018)","journal-title":"IEEE Trans. Softw. Eng."},{"key":"15_CR10","doi-asserted-by":"crossref","unstructured":"Alippi, C., Disabato, S., Roveri, M.: Moving convolutional neural networks to embedded systems: the alexnet and VGG-16 case. In: 2018 17th ACM\/IEEE International Conference on Information Processing in Sensor Networks (IPSN), pp. 212\u2013223. IEEE (2018)","DOI":"10.1109\/IPSN.2018.00049"},{"issue":"1","key":"15_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/sym11010001","volume":"11","author":"M Mateen","year":"2018","unstructured":"Mateen, M., Wen, J., Song, S., Huang, Z.: Fundus image classification using VGG-19 architecture with PCA and SVD. Symmetry 11(1), 1 (2018)","journal-title":"Symmetry"},{"issue":"2","key":"15_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-020-0114-9","volume":"1","author":"D Theckedath","year":"2020","unstructured":"Theckedath, D., Sedamkar, R.R.: Detecting affect states using VGG16, ResNet50 and SE-ResNet50 networks. SN Comput. Sci. 1(2), 1\u20137 (2020)","journal-title":"SN Comput. Sci."},{"issue":"4","key":"15_CR13","doi-asserted-by":"publisher","first-page":"2378","DOI":"10.11591\/eei.v11i4.3834","volume":"11","author":"AK Bitto","year":"2022","unstructured":"Bitto, A.K., Mahmud, I.: Multi categorical of common eye disease detect using convolutional neural network: a transfer learning approach. Bull. Electr. Eng. Inform. 11(4), 2378\u20132387 (2022). https:\/\/doi.org\/10.11591\/eei.v11i4.3834","journal-title":"Bull. Electr. Eng. Inform."},{"key":"15_CR14","doi-asserted-by":"publisher","unstructured":"Hasan, S., Rabbi, G., Islam, R., Imam Bijoy, H., Hakim, A.: Bangla font recognition using transfer learning method. In: 2022 International Conference on Inventive Computation Technologies (ICICT), pp. 57\u201362 (2022). https:\/\/doi.org\/10.1109\/ICICT54344.2022.9850765","DOI":"10.1109\/ICICT54344.2022.9850765"}],"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_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T19:06:36Z","timestamp":1686423996000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-34619-4_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031346187","9783031346194"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-34619-4_15","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)"}}]}}