{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T11:36:47Z","timestamp":1770464207624,"version":"3.49.0"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031346187","type":"print"},{"value":"9783031346194","type":"electronic"}],"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_17","type":"book-chapter","created":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T19:01:31Z","timestamp":1686423691000},"page":"201-212","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Lung Cancer Detection from\u00a0Histopathological Images Using Deep Learning"],"prefix":"10.1007","author":[{"given":"Rahul Deb","family":"Mohalder","sequence":"first","affiliation":[]},{"given":"Khandkar Asif","family":"Hossain","sequence":"additional","affiliation":[]},{"given":"Juliet Polok","family":"Sarkar","sequence":"additional","affiliation":[]},{"given":"Laboni","family":"Paul","sequence":"additional","affiliation":[]},{"given":"M.","family":"Raihan","sequence":"additional","affiliation":[]},{"given":"Kamrul Hasan","family":"Talukder","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,11]]},"reference":[{"key":"17_CR1","unstructured":"Lung cancer. www.verywellhealth.com\/lung-cancer-overview-4581940"},{"issue":"3","key":"17_CR2","doi-asserted-by":"publisher","first-page":"72","DOI":"10.5539\/gjhs.v8n3p72","volume":"8","author":"HA Alturkistani","year":"2016","unstructured":"Alturkistani, H.A., Tashkandi, F.M., Mohammedsaleh, Z.M.: Histological stains: a literature review and case study. Global J. Health Sci. 8(3), 72 (2016)","journal-title":"Global J. Health Sci."},{"key":"17_CR3","doi-asserted-by":"crossref","unstructured":"Anirudh, R., Thiagarajan, J.J., Bremer, T., Kim, H.: Lung nodule detection using 3D convolutional neural networks trained on weakly labeled data. In: Medical Imaging 2016: Computer-Aided Diagnosis, vol. 9785, p. 978532. International Society for Optics and Photonics (2016)","DOI":"10.1117\/12.2214876"},{"issue":"3","key":"17_CR4","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1007\/s00401-010-0725-7","volume":"120","author":"MJ Van den Bent","year":"2010","unstructured":"Van den Bent, M.J.: Interobserver variation of the histopathological diagnosis in clinical trials on glioma: a clinician\u2019s perspective. Acta Neuropathol. 120(3), 297\u2013304 (2010)","journal-title":"Acta Neuropathol."},{"key":"17_CR5","unstructured":"Borkowski, A.A., Bui, M.M., Thomas, L.B., Wilson, C.P., DeLand, L.A., Mastorides, S.M.: Lung and colon cancer histopathological image dataset (lc25000). arXiv preprint arXiv:1912.12142 (2019)"},{"key":"17_CR6","doi-asserted-by":"publisher","first-page":"8869","DOI":"10.1109\/ACCESS.2017.2694446","volume":"5","author":"M Chen","year":"2017","unstructured":"Chen, M., Hao, Y., Hwang, K., Wang, L., Wang, L.: Disease prediction by machine learning over big data from healthcare communities. IEEE Access 5, 8869\u20138879 (2017)","journal-title":"IEEE Access"},{"issue":"4","key":"17_CR7","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1038\/labinvest.2014.153","volume":"95","author":"LA Cooper","year":"2015","unstructured":"Cooper, L.A., Kong, J., Gutman, D.A., Dunn, W.D., Nalisnik, M., Brat, D.J.: Novel genotype-phenotype associations in human cancers enabled by advanced molecular platforms and computational analysis of whole slide images. Lab. Invest. 95(4), 366\u2013376 (2015)","journal-title":"Lab. Invest."},{"key":"17_CR8","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":"17_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1007\/978-3-319-66179-7_64","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2017","author":"J Ding","year":"2017","unstructured":"Ding, J., Li, A., Hu, Z., Wang, L.: Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 559\u2013567. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66179-7_64"},{"issue":"7","key":"17_CR10","doi-asserted-by":"publisher","first-page":"1558","DOI":"10.1109\/TBME.2016.2613502","volume":"64","author":"Q Dou","year":"2016","unstructured":"Dou, Q., Chen, H., Yu, L., Qin, J., Heng, P.A.: Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection. IEEE Trans. Biomed. Eng. 64(7), 1558\u20131567 (2016)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"17_CR11","doi-asserted-by":"crossref","unstructured":"Gao, F., Huang, T., Wang, J., Sun, J., Yang, E., Hussain, A.: Combining deep convolutional neural network and SVM to SAR image target recognition. In: 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 1082\u20131085. IEEE (2017)","DOI":"10.1109\/iThings-GreenCom-CPSCom-SmartData.2017.165"},{"issue":"5","key":"17_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/JPHOT.2021.3109016","volume":"13","author":"X Gao","year":"2021","unstructured":"Gao, X., et al.: Improvement of image classification by multiple optical scattering. IEEE Photonics J. 13(5), 1\u20135 (2021). https:\/\/doi.org\/10.1109\/JPHOT.2021.3109016","journal-title":"IEEE Photonics J."},{"key":"17_CR13","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT press, Cambridge (2016)"},{"key":"17_CR14","doi-asserted-by":"crossref","unstructured":"G\u00fcnaydin, \u00d6., G\u00fcnay, M., \u015eengel, \u00d6.: Comparison of lung cancer detection algorithms. In: 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), pp. 1\u20134. IEEE (2019)","DOI":"10.1109\/EBBT.2019.8741826"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Ivanov, A., Zhilenkov, A.: The prospects of use of deep learning neural networks in problems of dynamic images recognition. In: 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), pp. 886\u2013889. IEEE (2018)","DOI":"10.1109\/EIConRus.2018.8317230"},{"issue":"3","key":"17_CR16","doi-asserted-by":"publisher","first-page":"427","DOI":"10.3390\/app9030427","volume":"9","author":"G Jakimovski","year":"2019","unstructured":"Jakimovski, G., Davcev, D.: Using double convolution neural network for lung cancer stage detection. Appl. Sci. 9(3), 427 (2019)","journal-title":"Appl. Sci."},{"issue":"4","key":"17_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 nodule based on multigroup patch-based deep learning network. IEEE J. Biomed. Health Inform. 22(4), 1227\u20131237 (2017)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"17_CR18","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.bspc.2016.08.003","volume":"31","author":"C Kotsavasiloglou","year":"2017","unstructured":"Kotsavasiloglou, C., Kostikis, N., Hristu-Varsakelis, D., Arnaoutoglou, M.: Machine learning-based classification of simple drawing movements in Parkinson\u2019s disease. Biomed. Signal Process. Control 31, 174\u2013180 (2017)","journal-title":"Biomed. Signal Process. Control"},{"issue":"6","key":"17_CR19","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84\u201390 (2017)","journal-title":"Commun. ACM"},{"issue":"7553","key":"17_CR20","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"key":"17_CR21","doi-asserted-by":"publisher","first-page":"25657","DOI":"10.1109\/ACCESS.2022.3150924","volume":"10","author":"S Mehmood","year":"2022","unstructured":"Mehmood, S., et al.: Malignancy detection in lung and colon histopathology images using transfer learning with class selective image processing. IEEE Access 10, 25657\u201325668 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3150924","journal-title":"IEEE Access"},{"key":"17_CR22","doi-asserted-by":"publisher","unstructured":"Mohalder, R.D., Sarkar, J.P., Hossain, K.A., Paul, L., Raihan, M.: A deep learning based approach to predict lung cancer from histopathological images. In: 2021 International Conference on Electronics, Communications and Information Technology (ICECIT), pp. 1\u20134 (2021). https:\/\/doi.org\/10.1109\/ICECIT54077.2021.9641341","DOI":"10.1109\/ICECIT54077.2021.9641341"},{"key":"17_CR23","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.compchemeng.2017.06.011","volume":"106","author":"M Nilashi","year":"2017","unstructured":"Nilashi, M., Bin Ibrahim, O., Ahmadi, H., Shahmoradi, L.: An analytical method for diseases prediction using machine learning techniques. Comput. Chem. Eng. 106, 212\u2013223 (2017)","journal-title":"Comput. Chem. Eng."},{"key":"17_CR24","doi-asserted-by":"crossref","unstructured":"Phankokkruad, M.: Ensemble transfer learning for lung cancer detection. In: 2021 4th International Conference on Data Science and Information Technology, pp. 438\u2013442 (2021)","DOI":"10.1145\/3478905.3478995"},{"issue":"5","key":"17_CR25","doi-asserted-by":"crossref","first-page":"1594","DOI":"10.1109\/TCBB.2017.2727042","volume":"15","author":"N Sedaghat","year":"2017","unstructured":"Sedaghat, N., Fathy, M., Modarressi, M.H., Shojaie, A.: Combining supervised and unsupervised learning for improved miRNA target prediction. IEEE\/ACM Trans. Comput. Biol. Bioinf. 15(5), 1594\u20131604 (2017)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"issue":"5","key":"17_CR26","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1109\/TMI.2016.2536809","volume":"35","author":"AAA Setio","year":"2016","unstructured":"Setio, A.A.A., et al.: Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans. Med. Imaging 35(5), 1160\u20131169 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"17_CR27","doi-asserted-by":"crossref","unstructured":"Sung, H., et al.: Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J. Clin. 71(3), 209\u2013249 (2021)","DOI":"10.3322\/caac.21660"},{"key":"17_CR28","doi-asserted-by":"publisher","first-page":"10674","DOI":"10.1109\/ACCESS.2017.2706318","volume":"5","author":"J Zhang","year":"2017","unstructured":"Zhang, J., et al.: Coupling a fast Fourier transformation with a machine learning ensemble model to support recommendations for heart disease patients in a telehealth environment. IEEE Access 5, 10674\u201310685 (2017)","journal-title":"IEEE Access"},{"key":"17_CR29","doi-asserted-by":"publisher","unstructured":"Zia ur Rehman, M., Javaid, M., Shah, S.I.A., Gilani, S.O., Jamil, M., Butt, S.I.: An appraisal of nodules detection techniques for lung cancer in CT images. Biomed. Signal Process. Control, 41, 140\u2013151 (2018). https:\/\/doi.org\/10.1016\/j.bspc.2017.11.017, www.sciencedirect.com\/science\/article\/pii\/S1746809417302811","DOI":"10.1016\/j.bspc.2017.11.017"}],"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_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T02:36:18Z","timestamp":1729564578000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-34619-4_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031346187","9783031346194"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-34619-4_17","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"value":"1867-8211","type":"print"},{"value":"1867-822X","type":"electronic"}],"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)"}}]}}