{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T11:42:43Z","timestamp":1778845363239,"version":"3.51.4"},"publisher-location":"Cham","reference-count":45,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031832093","type":"print"},{"value":"9783031832109","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-83210-9_13","type":"book-chapter","created":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T19:16:49Z","timestamp":1741807009000},"page":"164-182","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Data Augmentation for\u00a0Improved Melanoma Classification in\u00a0Convolutional Neural Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5775-6536","authenticated-orcid":false,"given":"Pamela","family":"Hermosilla","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5755-6929","authenticated-orcid":false,"given":"Ricardo","family":"Soto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0978-0866","authenticated-orcid":false,"given":"Jeft\u00e9","family":"Ponce","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8052-1292","authenticated-orcid":false,"given":"Cristian","family":"Suazo Jara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5500-0188","authenticated-orcid":false,"given":"Broderick","family":"Crawford","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7945-9890","authenticated-orcid":false,"given":"Sebasti\u00e1n","family":"Contreras","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,13]]},"reference":[{"key":"13_CR1","doi-asserted-by":"crossref","unstructured":"Di Biasi, L., De Marco, F., Citarella, A.A., Castrill\u00f3n-Santana, M., Barra, P., Tortora, G.: Refactoring and performance analysis of the main CNN architectures: using false negative rate minimization to solve the clinical images melanoma detection problem. J. Med. Imaging 15(3) (2023)","DOI":"10.21203\/rs.3.rs-2229754\/v1"},{"issue":"2","key":"13_CR2","first-page":"89","volume":"28","author":"R Li","year":"2022","unstructured":"Li, R., et al.: An effective data augmentation strategy for CNN-based pest localization and recognition in the field. J. Agric. Eng. 28(2), 89\u201398 (2022)","journal-title":"J. Agric. Eng."},{"key":"13_CR3","first-page":"567","volume":"35","author":"N Milo\u0161evi\u0107","year":"2020","unstructured":"Milo\u0161evi\u0107, N., Rackovi\u0107, M.: Classification based on missing features in deep convolutional neural networks. Neural Netw. 35, 567\u2013580 (2020)","journal-title":"Neural Netw."},{"issue":"3","key":"13_CR4","first-page":"245","volume":"10","author":"S Benbakreti","year":"2023","unstructured":"Benbakreti, S., Benouis, M., Roumane, A., Benbakreti, S.: Impact of the data augmentation on the detection of brain tumor from MRI images based on CNN and pretrained models. J. Med. Imaging 10(3), 245\u2013258 (2023)","journal-title":"J. Med. Imaging"},{"issue":"4","key":"13_CR5","first-page":"567","volume":"12","author":"Q Yao","year":"2022","unstructured":"Yao, Q., Wang, Y., Yang, Y.: Underwater acoustic target recognition based on data augmentation and residual CNN. IEEE J. Oceanic Eng. 12(4), 567\u2013580 (2022)","journal-title":"IEEE J. Oceanic Eng."},{"issue":"2","key":"13_CR6","first-page":"123","volume":"45","author":"H-C Chu","year":"2023","unstructured":"Chu, H.-C., Zhang, Y.-L., Chiang, H.-C.: A CNN sound classification mechanism using data augmentation. J. Acoust. Soc. Am. 45(2), 123\u2013135 (2023)","journal-title":"J. Acoust. Soc. Am."},{"key":"13_CR7","unstructured":"Li, D., Xie, W., Wang, B.: Data augmentation and layered deformable mask R-CNN-based detection of wood defects. J. Wood Sci. 10(4), 567\u2013580 (2022)"},{"issue":"2","key":"13_CR8","first-page":"123","volume":"45","author":"R Li","year":"2023","unstructured":"Li, R., et al.: An effective data augmentation strategy for CNN-based pest localization and recognition in the field. J. Agric. Eng. 45(2), 123\u2013135 (2023)","journal-title":"J. Agric. Eng."},{"issue":"2","key":"13_CR9","first-page":"123","volume":"10","author":"E Arslan","year":"2023","unstructured":"Arslan, E., Schulz, J., Rai, K.: Machine learning in epigenomics: insights into cancer biology and medicine. J. Epigenetics 10(2), 123\u2013135 (2023)","journal-title":"J. Epigenetics"},{"issue":"4","key":"13_CR10","doi-asserted-by":"publisher","first-page":"862","DOI":"10.1007\/s10278-021-00478-7","volume":"34","author":"R Hao","year":"2021","unstructured":"Hao, R., Namdar, K., Liu, L., Haider, M.A., Khalvati, F.: A comprehensive study of data augmentation strategies for prostate cancer detection in diffusion-weighted MRI using convolutional neural networks. J. Digit. Imaging 34(4), 862\u2013876 (2021). https:\/\/doi.org\/10.1007\/s10278-021-00478-7","journal-title":"J. Digit. Imaging"},{"key":"13_CR11","unstructured":"Wong, J.R., Harris, J.K.,\u00a0Rodriguez-Galindo, C.: The role of immune checkpoint inhibitors in melanoma. Pediatric Blood Cancer 66(11), e27981 (2019). https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1002\/pbc.27981"},{"key":"13_CR12","unstructured":"American Cancer Society: \u201cWhat is cancer?\u201d (2020). https:\/\/www.cancer.org\/cancer\/cancer-basics\/what-is-cancer.html. Accessed 18 May 2024"},{"key":"13_CR13","unstructured":"National Cancer Institute: \u201cCancer statistics,\u201d (2021). https:\/\/www.cancer.gov\/about-cancer\/understanding\/statistics. Accessed 18 May 2024"},{"key":"13_CR14","unstructured":"Skin Cancer Foundation: \u201cThe ABCDES of melanoma,\u201d (2021). https:\/\/www.skincancer.org\/skin-cancer-information\/melanoma\/melanoma-warning-signs-and-images\/do-you-know-your-abcdes\/. Accessed 18 May 2024"},{"key":"13_CR15","unstructured":"American Academy of Dermatology: \u201cSkin cancer symptoms: What to look for,\u201d (2021). https:\/\/www.aad.org\/public\/diseases\/skin-cancer\/types\/common\/signs. Accessed 18 May 2024"},{"key":"13_CR16","doi-asserted-by":"crossref","unstructured":"LeCun, Y.,\u00a0Bengio, Y.,\u00a0Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015). https:\/\/www.nature.com\/articles\/nature14539","DOI":"10.1038\/nature14539"},{"key":"13_CR17","unstructured":"Goodfellow, I.,\u00a0Bengio, Y.,\u00a0Courville, A.,\u00a0Bengio, Y.: Deep Learning. MIT Press (2016). https:\/\/www.deeplearningbook.org\/"},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. In: Proceedings of the IEEE, vol.\u00a086, no.\u00a011, pp. 2278\u20132324 (1998). https:\/\/ieeexplore.ieee.org\/document\/726791","DOI":"10.1109\/5.726791"},{"key":"13_CR19","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012). https:\/\/papers.nips.cc\/paper\/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf"},{"key":"13_CR20","doi-asserted-by":"crossref","unstructured":"Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533\u2013536 (1986). https:\/\/www.nature.com\/articles\/323533a0","DOI":"10.1038\/323533a0"},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157\u2013166 (1994). https:\/\/ieeexplore.ieee.org\/document\/279181","DOI":"10.1109\/72.279181"},{"key":"13_CR22","doi-asserted-by":"crossref","unstructured":"Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manag. 45(4), 427\u2013437 (2009). https:\/\/www.sciencedirect.com\/science\/article\/pii\/S030645730800179X","DOI":"10.1016\/j.ipm.2009.03.002"},{"key":"13_CR23","unstructured":"Powers, D.M.: Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation. J. Mach. Learn. Technol. 2(1), 37\u201363 (2011). https:\/\/arxiv.org\/abs\/2010.16061"},{"key":"13_CR24","doi-asserted-by":"crossref","unstructured":"Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 60 (2019). https:\/\/link.springer.com\/article\/10.1186\/s40537-019-0197-0","DOI":"10.1186\/s40537-019-0197-0"},{"key":"13_CR25","unstructured":"Wang, J., Perez, L.: The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621 (2017). https:\/\/arxiv.org\/abs\/1712.04621"},{"key":"13_CR26","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: CutMix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6023\u20136032 (2019). https:\/\/openaccess.thecvf.com\/content_ICCV_2019\/html\/Yun_CutMix_Regularization_Strategy_to_Train_Strong_Classifiers_with_Localizable_Features_ICCV_2019_paper.html","DOI":"10.1109\/ICCV.2019.00612"},{"key":"13_CR27","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: Random erasing data augmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a034, no.\u00a007, pp. 13001\u201313008 (2020). https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/7019","DOI":"10.1609\/aaai.v34i07.7000"},{"key":"13_CR28","doi-asserted-by":"publisher","unstructured":"Zhang, C., Aamir, M., Guan, Y. et al.: Enhancing lung cancer diagnosis with data fusion and mobile edge computing using DenseNet and CNN. J. Cloud Comp. 13, 91 (2024). https:\/\/doi.org\/10.1186\/s13677-024-00597-w","DOI":"10.1186\/s13677-024-00597-w"},{"key":"13_CR29","doi-asserted-by":"crossref","unstructured":"Girdhar, N., Sinha, A., Gupta, S.: Densenet-II: an improved deep convolutional neural network for melanoma cancer detection. J. Cloud Comput. (2022)","DOI":"10.1007\/s00500-022-07406-z"},{"issue":"3","key":"13_CR30","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1007\/s13042-020-01194-4","volume":"12","author":"B Chen","year":"2020","unstructured":"Chen, B., Zhao, T., Liu, J., Lin, L.: Multipath feature recalibration DenseNet for image classification. Int. J. Mach. Learn. Cybern. 12(3), 651\u2013660 (2020). https:\/\/doi.org\/10.1007\/s13042-020-01194-4","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"13_CR31","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1186\/s13640-020-00521-7","volume":"2020","author":"K Zhang","year":"2020","unstructured":"Zhang, K., Guo, Y., Wang, X., Yuan, J., Ding, Q.: Multiple feature reweight DenseNet for image classification. EURASIP J. Image Video Process. 2020, 39 (2020)","journal-title":"EURASIP J. Image Video Process."},{"key":"13_CR32","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.ejca.2019.06.013","volume":"119","author":"RC Maron","year":"2019","unstructured":"Maron, R.C., et al.: Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks. Eur. J. Cancer 119, 57\u201365 (2019)","journal-title":"Eur. J. Cancer"},{"key":"13_CR33","doi-asserted-by":"publisher","first-page":"1319","DOI":"10.1007\/s11036-020-01550-2","volume":"25","author":"M Kumar","year":"2020","unstructured":"Kumar, M., Alshehri, M., AlGhamdi, R., Sharma, P., Deep, V.: A De-ANN inspired skin cancer detection approach using fuzzy C-means clustering. Mob. Netw. Appl. 25, 1319\u20131329 (2020)","journal-title":"Mob. Netw. Appl."},{"issue":"3","key":"13_CR34","first-page":"4","volume":"11","author":"J Yoo","year":"2023","unstructured":"Yoo, J., Kang, S.: Class-adaptive data augmentation for image classification. IEEE Access 11(3), 4\u20139 (2023)","journal-title":"IEEE Access"},{"key":"13_CR35","doi-asserted-by":"crossref","unstructured":"Montaha, S., et al.: MNet-10: a robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique. IEEE Access 11, 12345\u201312356 (2023)","DOI":"10.3389\/fmed.2022.924979"},{"key":"13_CR36","doi-asserted-by":"crossref","unstructured":"He, Q., Ruan, H., Pan, J., Lyu, X.: A method to detect internal leakage of hydraulic cylinder by combining data augmentation and multiscale residual CNN. J. Eng. 2023, e12301 (2023). received: 7 May 2023, Revised: 5 August 2023, Accepted: 9 August 2023","DOI":"10.1049\/tje2.12301"},{"key":"13_CR37","doi-asserted-by":"crossref","unstructured":"Ramadan, S.Z.: Using convolutional neural network with cheat sheet and data augmentation to detect breast cancer in mammograms. Comput. Math. Methods Med. 2020, 9 (2020). received: 8 May 2020, Revised: 22 September 2020, Accepted: 20 October 2020, Published: 28 October 2020","DOI":"10.1155\/2020\/9523404"},{"key":"13_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119838","volume":"654","author":"H Eghbal-zadeh","year":"2024","unstructured":"Eghbal-zadeh, H., et al.: Rethinking data augmentation for adversarial robustness. Inf. Sci. 654, 119838 (2024)","journal-title":"Inf. Sci."},{"key":"13_CR39","unstructured":"Huang, S.G., Chung, M.K., Qiu, A.: \u201cFast mesh data augmentation via chebyshev polynomial of spectral filtering\u201d for the Alzheimer\u2019s Disease Neuroimaging Initiative, vol. 1 (2024). department of Biomedical Engineering, National University of Singapore, Singapore; Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI 53706, United States of America; Institute of Data Science, National University of Singapore, Singapore; The N.1 Institute for Health, National University of Singapore, Singapore; The Johns Hopkins University, MD, USA (2024)"},{"key":"13_CR40","unstructured":"Lim, G.,\u00a0Yamada, J.,\u00a0Jang, T.,\u00a0Lee, S.,\u00a0Kim, J., Hwang S.J.: Fast autoaugment. In: International Conference on Learning Representations (ICLR) (2019). https:\/\/openreview.net\/forum?id=rJBiunlA5m"},{"key":"13_CR41","doi-asserted-by":"crossref","unstructured":"Cubuk, E.D., Zoph, B., Shlens, J., Le, Q.V.: Randaugment: practical automated data augmentation with a reduced search space. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8019\u20138028 (2020)","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"13_CR42","unstructured":"Xie, Q., Dai, Z., Hovy, E., Luong, T., Le, Q.: Unsupervised data augmentation for consistency training. In: Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020, pp. 451\u2013461. Springer (2020)"},{"key":"13_CR43","unstructured":"Wei, B., Hu, G., Ji, S.: Adversarial attacks and defenses in images, graphs and text: a review. Inf. Fusion 64, 1\u201325 (2020)"},{"issue":"2","key":"13_CR44","volume":"58","author":"T Li","year":"2021","unstructured":"Li, T., Jin, J., Yan, X., Qiu, X.: Self-supervised learning and data augmentation for low-resource named entity recognition. Inf. Process. Manag. 58(2), 102493 (2021)","journal-title":"Inf. Process. Manag."},{"issue":"6","key":"13_CR45","first-page":"5651","volume":"13","author":"X Wu","year":"2022","unstructured":"Wu, X., Wu, J., Li, J.: A novel data augmentation method for medical image classification with deep learning. J. Ambient. Intell. Humaniz. Comput. 13(6), 5651\u20135661 (2022)","journal-title":"J. Ambient. Intell. Humaniz. Comput."}],"container-title":["Communications in Computer and Information Science","Advanced Research in Technologies, Information, Innovation and Sustainability"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-83210-9_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T19:17:02Z","timestamp":1741807022000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-83210-9_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031832093","9783031832109"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-83210-9_13","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"13 March 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare\u00a0that are relevant to the content of this article(If EquinOCS, our proceedings submission system, is used, then the disclaimer can be provided directly in the system.)","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ARTIIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Santiago de Chile","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chile","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":"20 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"artiis2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.artiis.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}