{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T09:42:51Z","timestamp":1771234971809,"version":"3.50.1"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031646041","type":"print"},{"value":"9783031646058","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-64605-8_11","type":"book-chapter","created":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T05:01:59Z","timestamp":1719810119000},"page":"151-164","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Enhancing Explainability in\u00a0Oral Cancer Detection with\u00a0Grad-CAM Visualizations"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0300-3341","authenticated-orcid":false,"given":"Arnaldo V. Barros","family":"da Silva","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2422-2164","authenticated-orcid":false,"given":"Cristina","family":"Saldivia-Siracusa","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7825-560X","authenticated-orcid":false,"given":"Eduardo Santos Carlos","family":"de Souza","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3725-8051","authenticated-orcid":false,"given":"Anna Lu\u00edza Damaceno","family":"Ara\u00fajo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6677-0065","authenticated-orcid":false,"given":"Marcio Ajudarte","family":"Lopes","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1840-4911","authenticated-orcid":false,"given":"Pablo Agustin","family":"Vargas","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0481-156X","authenticated-orcid":false,"given":"Luiz Paulo","family":"Kowalski","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2040-6617","authenticated-orcid":false,"given":"Alan Roger","family":"Santos-Silva","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4765-6459","authenticated-orcid":false,"given":"Andr\u00e9 C. P. L. F.","family":"de Carvalho","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8147-554X","authenticated-orcid":false,"given":"Marcos G.","family":"Quiles","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,2]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Albawi, S., Mohammed, T.A., Al-Zawi, S.: Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology (ICET), pp.\u00a01\u20136. IEEE (2017)","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"issue":"11","key":"11_CR2","doi-asserted-by":"publisher","first-page":"1932","DOI":"10.3390\/diagnostics13111932","volume":"13","author":"B Aldughayfiq","year":"2023","unstructured":"Aldughayfiq, B., Ashfaq, F., Jhanjhi, N., Humayun, M.: Explainable AI for retinoblastoma diagnosis: interpreting deep learning models with lime and shap. Diagnostics 13(11), 1932 (2023)","journal-title":"Diagnostics"},{"issue":"7623","key":"11_CR3","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1038\/538020a","volume":"538","author":"D Castelvecchi","year":"2016","unstructured":"Castelvecchi, D.: Can we open the black box of AI? Nat. News 538(7623), 20 (2016)","journal-title":"Nat. News"},{"key":"11_CR4","doi-asserted-by":"crossref","unstructured":"Chattopadhay, A., Sarkar, A., Howlader, P., Balasubramanian, V.N.: Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 839\u2013847. IEEE (2018)","DOI":"10.1109\/WACV.2018.00097"},{"key":"11_CR5","unstructured":"Cian, D., van Gemert, J., Lengyel, A.: Evaluating the performance of the lime and grad-cam explanation methods on a LEGO multi-label image classification task. arXiv preprint arXiv:2008.01584 (2020)"},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Deng, J., et al.: 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"},{"issue":"4\u20135","key":"11_CR7","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.compmedimag.2007.02.002","volume":"31","author":"K Doi","year":"2007","unstructured":"Doi, K.: Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput. Med. Imaging Graph. 31(4\u20135), 198\u2013211 (2007)","journal-title":"Comput. Med. Imaging Graph."},{"issue":"1","key":"11_CR8","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1186\/s13000-023-01407-8","volume":"18","author":"M D\u00f6rrich","year":"2023","unstructured":"D\u00f6rrich, M., et al.: Explainable convolutional neural networks for assessing head and neck cancer histopathology. Diagn. Pathol. 18(1), 121 (2023)","journal-title":"Diagn. Pathol."},{"issue":"1","key":"11_CR9","doi-asserted-by":"publisher","first-page":"015001","DOI":"10.1117\/1.JBO.27.1.015001","volume":"27","author":"KC Figueroa","year":"2022","unstructured":"Figueroa, K.C., et al.: Interpretable deep learning approach for oral cancer classification using guided attention inference network. J. Biomed. Opt. 27(1), 015001\u2013015001 (2022)","journal-title":"J. Biomed. Opt."},{"key":"11_CR10","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.ejca.2022.02.025","volume":"167","author":"K Hauser","year":"2022","unstructured":"Hauser, K., et al.: Explainable artificial intelligence in skin cancer recognition: a systematic review. Eur. J. Cancer 167, 54\u201369 (2022)","journal-title":"Eur. J. Cancer"},{"issue":"7553","key":"11_CR11","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":"11_CR12","doi-asserted-by":"crossref","unstructured":"Li, K., Wu, Z., Peng, K.C., Ernst, J., Fu, Y.: Tell me where to look: guided attention inference network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9215\u20139223 (2018)","DOI":"10.1109\/CVPR.2018.00960"},{"key":"11_CR13","unstructured":"Li, S., Jiao, J., Han, Y., Weissman, T.: Demystifying resnet. arXiv preprint arXiv:1611.01186 (2016)"},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976\u201311986 (2022)","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"11_CR15","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765\u20134774. Curran Associates, Inc. (2017). http:\/\/papers.nips.cc\/paper\/7062-a-unified-approach-to-interpreting-model-predictions.pdf"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"McAuliffe, M.J., Lalonde, F.M., McGarry, D., Gandler, W., Csaky, K., Trus, B.L.: Medical image processing, analysis and visualization in clinical research. In: Proceedings 14th IEEE Symposium on Computer-Based Medical Systems, CBMS 2001, pp. 381\u2013386. IEEE (2001)","DOI":"10.1109\/CBMS.2001.941749"},{"key":"11_CR17","doi-asserted-by":"publisher","unstructured":"Pratiher, S., Chattoraj, S., Nawn, D., Pal, M., Paul, R.R., Konik, H., Chatterjee, J.: A multi-scale context aggregation enriched mlp-mixer model for oral cancer screening from oral sub-epithelial connective tissues. In: 2022 30th European Signal Processing Conference (EUSIPCO), pp. 1323\u20131327 (2022). https:\/\/doi.org\/10.23919\/EUSIPCO55093.2022.9909942","DOI":"10.23919\/EUSIPCO55093.2022.9909942"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \"why should I trust you?\": Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13-17 August 2016, pp. 1135\u20131144 (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"11_CR19","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"},{"issue":"1","key":"11_CR20","first-page":"1","volume":"34","author":"M Soluk-Tekke\u015fin","year":"2018","unstructured":"Soluk-Tekke\u015fin, M., Wright, J.M.: The world health organization classification of odontogenic lesions: a summary of the changes of the 2017 (4th) edition. Turk Patoloji Derg 34(1), 1\u201318 (2018)","journal-title":"Turk Patoloji Derg"},{"issue":"9","key":"11_CR21","doi-asserted-by":"publisher","first-page":"1849","DOI":"10.1158\/1055-9965.EPI-22-0114","volume":"31","author":"EP Tranby","year":"2022","unstructured":"Tranby, E.P., et al.: Oral cancer prevalence, mortality, and costs in medicaid and commercial insurance claims data. Cancer Epidemiol. Biomark. Reven. 31(9), 1849\u20131857 (2022)","journal-title":"Cancer Epidemiol. Biomark. Reven."},{"issue":"1","key":"11_CR22","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":"11_CR23","doi-asserted-by":"publisher","first-page":"105262","DOI":"10.1109\/ACCESS.2023.3319068","volume":"11","author":"D Varam","year":"2023","unstructured":"Varam, D., et al.: Wireless capsule endoscopy image classification: an explainable AI approach. IEEE Access 11, 105262\u2013105280 (2023)","journal-title":"IEEE Access"},{"key":"11_CR24","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"issue":"9","key":"11_CR25","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1111\/jop.13227","volume":"50","author":"K Warin","year":"2021","unstructured":"Warin, K., Limprasert, W., Suebnukarn, S., Jinaporntham, S., Jantana, P.: Automatic classification and detection of oral cancer in photographic images using deep learning algorithms. J. Oral Pathol. Med. 50(9), 911\u2013918 (2021)","journal-title":"J. Oral Pathol. Med."},{"issue":"8","key":"11_CR26","doi-asserted-by":"publisher","first-page":"e0273508","DOI":"10.1371\/journal.pone.0273508","volume":"17","author":"K Warin","year":"2022","unstructured":"Warin, K., Limprasert, W., Suebnukarn, S., Jinaporntham, S., Jantana, P., Vicharueang, S.: Ai-based analysis of oral lesions using novel deep convolutional neural networks for early detection of oral cancer. PLoS ONE 17(8), e0273508 (2022)","journal-title":"PLoS ONE"},{"issue":"5","key":"11_CR27","doi-asserted-by":"publisher","first-page":"1035","DOI":"10.3390\/agronomy12051035","volume":"12","author":"K Wei","year":"2022","unstructured":"Wei, K., Chen, B., Zhang, J., Fan, S., Wu, K., Liu, G., Chen, D.: Explainable deep learning study for leaf disease classification. Agronomy 12(5), 1035 (2022)","journal-title":"Agronomy"},{"key":"11_CR28","doi-asserted-by":"publisher","unstructured":"Welikala, R.A., et al.: Automated detection and classification of oral lesions using deep learning for early detection of oral cancer. IEEE Access 8, 132677\u2013132693 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3010180","DOI":"10.1109\/ACCESS.2020.3010180"},{"key":"11_CR29","doi-asserted-by":"publisher","first-page":"109098","DOI":"10.1016\/j.jneumeth.2021.109098","volume":"353","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Hong, D., McClement, D., Oladosu, O., Pridham, G., Slaney, G.: Grad-cam helps interpret the deep learning models trained to classify multiple sclerosis types using clinical brain magnetic resonance imaging. J. Neurosci. Methods 353, 109098 (2021)","journal-title":"J. Neurosci. Methods"},{"key":"11_CR30","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921\u20132929 (2016)","DOI":"10.1109\/CVPR.2016.319"}],"container-title":["Lecture Notes in Computer Science","Computational Science and Its Applications \u2013 ICCSA 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-64605-8_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T04:08:00Z","timestamp":1732334880000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-64605-8_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031646041","9783031646058"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-64605-8_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"2 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare no competing interests relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ICCSA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science and Its Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hanoi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsa2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}