{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T10:29:09Z","timestamp":1769768949129,"version":"3.49.0"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032159892","type":"print"},{"value":"9783032159908","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-15990-8_1","type":"book-chapter","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T19:59:26Z","timestamp":1769716766000},"page":"3-18","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Fully Automatic Approach for\u00a0COVID-19 Diagnosis in\u00a0CT Imaging: Integrating Lung Segmentation, Fine-Tuning and\u00a0Grad-CAM Visualization"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3854-7178","authenticated-orcid":false,"given":"Matheus A.","family":"dos Santos","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3595-6680","authenticated-orcid":false,"given":"I\u00e1gson Carlos L.","family":"Silva","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9388-8146","authenticated-orcid":false,"given":"Lucas","family":"de O. Santos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7913-0008","authenticated-orcid":false,"given":"Eliz\u00e2ngela","family":"de S. Rebou\u00e7as","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1878-5489","authenticated-orcid":false,"given":"Pedro Pedrosa Rebou\u00e7as","family":"Filho","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2631-9223","authenticated-orcid":false,"given":"Houbing H.","family":"Song","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,30]]},"reference":[{"issue":"2","key":"1_CR1","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1080\/10494820.2020.1813180","volume":"31","author":"OB Adedoyin","year":"2023","unstructured":"Adedoyin, O.B., Soykan, E.: Covid-19 pandemic and online learning: the challenges and opportunities. Interact. Learn. Environ. 31(2), 863\u2013875 (2023)","journal-title":"Interact. Learn. Environ."},{"issue":"1","key":"1_CR2","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1023\/A:1022689900470","volume":"6","author":"DW Aha","year":"1991","unstructured":"Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Mach. Learn. 6(1), 37\u201366 (1991)","journal-title":"Mach. Learn."},{"key":"1_CR3","doi-asserted-by":"publisher","unstructured":"Alshazly, H., Linse, C., Barth, E., Martinetz, T.: Explainable Covid-19 detection using chest CT scans and deep learning. Sensors 21(2) (2021). https:\/\/doi.org\/10.3390\/s21020455, https:\/\/www.mdpi.com\/1424-8220\/21\/2\/455","DOI":"10.3390\/s21020455"},{"issue":"2","key":"1_CR4","doi-asserted-by":"publisher","first-page":"455","DOI":"10.3390\/s21020455","volume":"21","author":"H Alshazly","year":"2021","unstructured":"Alshazly, H., Linse, C., Barth, E., Martinetz, T.: Explainable Covid-19 detection using chest CT scans and deep learning. Sensors 21(2), 455 (2021)","journal-title":"Sensors"},{"key":"1_CR5","unstructured":"Novel Coronavirus. https:\/\/www.who.int\/emergencies\/diseases\/novel-coronavirus-2019. Accessed Oct 2020"},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"1_CR7","unstructured":"Fernandez, F.G.: TorchCAM: class activation explorer, March 2020. https:\/\/github.com\/frgfm\/torch-cam"},{"key":"1_CR8","unstructured":"Girshick, R., Radosavovic, I., Gkioxari, G., Dollar, P., He, K.: Detectron (2018)"},{"issue":"3","key":"1_CR9","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1109\/TBME.2021.3117407","volume":"69","author":"H Guan","year":"2021","unstructured":"Guan, H., Liu, M.: Domain adaptation for medical image analysis: a survey. IEEE Trans. Biomed. Eng. 69(3), 1173\u20131185 (2021)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Han, T., et al.: Internet of medical things\u2013based on deep learning techniques for segmentation of lung and stroke regions in CT scans. IEEE Access 8, 71117\u201371135 (2020)","DOI":"10.1109\/ACCESS.2020.2987932"},{"key":"1_CR11","unstructured":"Haykin, S.S., et\u00a0al.: Neural Networks and Learning Machines (2009)"},{"key":"1_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"1_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":"1_CR14","doi-asserted-by":"crossref","unstructured":"He, X., et al.: Sample-efficient deep learning for Covid-19 diagnosis based on CT scans. medRxiv (2020)","DOI":"10.1101\/2020.04.13.20063941"},{"issue":"8","key":"1_CR15","doi-asserted-by":"publisher","first-page":"832","DOI":"10.1109\/34.709601","volume":"20","author":"TK Ho","year":"1998","unstructured":"Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832\u2013844 (1998)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1_CR16","unstructured":"Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)"},{"key":"1_CR17","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der\u00a0Maaten, 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"},{"issue":"15","key":"1_CR18","doi-asserted-by":"publisher","first-page":"5682","DOI":"10.1080\/07391102.2020.1788642","volume":"39","author":"A Jaiswal","year":"2021","unstructured":"Jaiswal, A., Gianchandani, N., Singh, D., Kumar, V., Kaur, M.: Classification of the Covid-19 infected patients using DenseNet201 based deep transfer learning. J. Biomol. Struct. Dyn. 39(15), 5682\u20135689 (2021)","journal-title":"J. Biomol. Struct. Dyn."},{"key":"1_CR19","doi-asserted-by":"crossref","unstructured":"Joshi, K.K., Gupta, K., Agrawal, J.: An efficient transfer learning approach for prediction and classification of SARS\u2013COVID-19. Multimedia Tools Appl., 1\u201323 (2023)","DOI":"10.1007\/s11042-023-17086-y"},{"key":"1_CR20","doi-asserted-by":"publisher","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"1_CR21","doi-asserted-by":"publisher","first-page":"107918","DOI":"10.1016\/j.asoc.2021.107918","volume":"113","author":"H Naeem","year":"2021","unstructured":"Naeem, H., Bin-Salem, A.A.: A CNN-LSTM network with multi-level feature extraction-based approach for automated detection of coronavirus from CT scan and X-ray images. Appl. Soft Comput. 113, 107918 (2021)","journal-title":"Appl. Soft Comput."},{"issue":"9","key":"1_CR22","doi-asserted-by":"publisher","first-page":"9494","DOI":"10.1007\/s11227-020-03575-6","volume":"77","author":"EF Ohata","year":"2021","unstructured":"Ohata, E.F., Chagas, J.V.S., Bezerra, G.M., Hassan, M.M., de Albuquerque, V.H.C., Filho, P.P.R.: A novel transfer learning approach for the classification of histological images of colorectal cancer. J. Supercomput. 77(9), 9494\u20139519 (2021). https:\/\/doi.org\/10.1007\/s11227-020-03575-6","journal-title":"J. Supercomput."},{"key":"1_CR23","doi-asserted-by":"crossref","unstructured":"Orenstein, E.C., Beijbom, O.: Transfer learning and deep feature extraction for planktonic image data sets. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1082\u20131088. IEEE (2017)","DOI":"10.1109\/WACV.2017.125"},{"key":"1_CR24","doi-asserted-by":"publisher","unstructured":"Panwar, H., Gupta, P., Siddiqui, M.K., Morales-Menendez, R., Bhardwaj, P., Singh, V.: A deep learning and grad-CAM based color visualization approach for fast detection of Covid-19 cases using chest X-ray and CT-scan images. Chaos Solitons Fractals 140, 110190 (2020). https:\/\/doi.org\/10.1016\/j.chaos.2020.110190, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0960077920305865","DOI":"10.1016\/j.chaos.2020.110190"},{"key":"1_CR25","doi-asserted-by":"crossref","unstructured":"Rebou\u00e7as Filho, P.P., da Silva Barros, A.C., Almeida, J.S., Rodrigues, J., de Albuquerque, V.H.C.: A new effective and powerful medical image segmentation algorithm based on optimum path snakes. Appl. Soft Comput. 76, 649\u2013670 (2019)","DOI":"10.1016\/j.asoc.2018.10.057"},{"key":"1_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-020-01562-1","volume":"44","author":"K Santosh","year":"2020","unstructured":"Santosh, K.: AI-driven tools for coronavirus outbreak: need of active learning and cross-population train\/test models on multitudinal\/multimodal data. J. Med. Syst. 44, 1\u20135 (2020)","journal-title":"J. Med. Syst."},{"key":"1_CR27","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"1_CR28","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.cytogfr.2023.11.005","volume":"76","author":"TH Senevirathne","year":"2024","unstructured":"Senevirathne, T.H., Wekking, D., Swain, J.W., Solinas, C., De Silva, P.: Covid-19: from emerging variants to vaccination. Cytokine Growth Factor Rev. 76, 127\u2013141 (2024)","journal-title":"Cytokine Growth Factor Rev."},{"key":"1_CR29","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"1_CR30","unstructured":"Soares, E., Angelov, P., Biaso, S., Froes, M.H., Abe, D.K.: SARS-COV-2 CT-scan dataset: a large dataset of real patients CT scans for SARS-COV-2 identification. medRxiv (2020)"},{"issue":"5","key":"1_CR31","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","volume":"35","author":"N Tajbakhsh","year":"2016","unstructured":"Tajbakhsh, N., et al.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging 35(5), 1299\u20131312 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1_CR32","unstructured":"Theodoridis, S., Koutroumbas, K.: Pattern Recognition. Academic Press, Burlington, MA (2008). [Google Scholar]"},{"key":"1_CR33","unstructured":"Vapnik, V.N.: Statistical Learning Theory (1998)"},{"issue":"10","key":"1_CR34","doi-asserted-by":"publisher","first-page":"2806","DOI":"10.1109\/JBHI.2020.3023246","volume":"24","author":"Z Wang","year":"2020","unstructured":"Wang, Z., Liu, Q., Dou, Q.: Contrastive cross-site learning with redesigned net for Covid-19 CT classification. IEEE J. Biomed. Health Inform. 24(10), 2806\u20132813 (2020)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"1_CR35","unstructured":"Wu, Y., Kirillov, A., Massa, F., Lo, W.Y., Girshick, R.: Detectron2 (2019)"},{"key":"1_CR36","doi-asserted-by":"crossref","unstructured":"Xu, Y., et al.: New fully automatic approach for tissue identification in histopathological examinations using transfer learning. IET Image Processing 16(11), 2875\u20132889 (2022)","DOI":"10.1049\/ipr2.12449"},{"key":"1_CR37","doi-asserted-by":"publisher","unstructured":"Zhao, W., Jiang, W., Qiu, X.: Deep learning for covid-19 detection based on CT images. Sci. Rep. 11(1), 14353 (2021). https:\/\/doi.org\/10.1038\/s41598-021-93832-2","DOI":"10.1038\/s41598-021-93832-2"}],"container-title":["Lecture Notes in Computer Science","Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-15990-8_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T19:59:30Z","timestamp":1769716770000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-15990-8_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032159892","9783032159908"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-15990-8_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"30 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"BRACIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazilian Conference on Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Fortaleza-CE","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazil","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bracis2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/bracis.sbc.org.br\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}