{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T16:40:52Z","timestamp":1779381652050,"version":"3.53.1"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031782008","type":"print"},{"value":"9783031782015","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"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\/978-3-031-78201-5_10","type":"book-chapter","created":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T15:08:22Z","timestamp":1733065702000},"page":"147-162","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Classification of\u00a0Cutaneous Diseases: A\u00a0Systematic Study on\u00a0Real-Time Captured Images Using Deep Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7964-1083","authenticated-orcid":false,"given":"Bhavik","family":"Kanekar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1647-8012","authenticated-orcid":false,"given":"Jay","family":"Sawant","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8813-2858","authenticated-orcid":false,"given":"Niti","family":"Chikhale","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0140-0700","authenticated-orcid":false,"given":"Paras","family":"Dhotre","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sushil","family":"Savant","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gajanan","family":"Nagare","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9795-8335","authenticated-orcid":false,"given":"Kshitij","family":"Jadhav","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,2]]},"reference":[{"key":"10_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2023\/5869513","volume":"2023","author":"A Al-Rasheed","year":"2023","unstructured":"Al-Rasheed, A., Ksibi, A., Ayadi, M., Alzahrani, A.I., Mamun Elahi, M.: An ensemble of transfer learning models for the prediction of skin lesions with conditional generative adversarial networks. Contrast Media Mol. Imaging 2023, 1\u201315 (2023)","journal-title":"Contrast Media Mol. Imaging"},{"key":"10_CR2","unstructured":"Arjovsky, M., Bottou, L.: Towards principled methods for training generative adversarial networks. arXiv preprint arXiv:1701.04862 (2017)"},{"key":"10_CR3","unstructured":"Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: Precup, D., Teh, Y.W. (eds.) Proceedings of the 34th International Conference on Machine Learning, vol.\u00a070, pp. 214\u2013223 (2017). https:\/\/proceedings.mlr.press\/v70\/arjovsky17a.html"},{"key":"10_CR4","doi-asserted-by":"publisher","unstructured":"Back, S., et al.: Robust skin disease classification by distilling deep neural network ensemble for the mobile diagnosis of herpes zoster. IEEE Access 20156\u201320169 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3054403","DOI":"10.1109\/ACCESS.2021.3054403"},{"key":"10_CR5","unstructured":"Bengio, Y., Yao, L., Alain, G., Vincent, P.: Generalized denoising auto-encoders as generative models. In: Advances in Neural Information Processing Systems, vol. 26 (2013)"},{"issue":"1","key":"10_CR6","first-page":"5287","volume":"22","author":"G Biau","year":"2021","unstructured":"Biau, G., Sangnier, M., Tanielian, U.: Some theoretical insights into Wasserstein GANs. J. Mach. Learn. Res. 22(1), 5287\u20135331 (2021)","journal-title":"J. Mach. Learn. Res."},{"key":"10_CR7","doi-asserted-by":"publisher","unstructured":"Codella, N.C.F., et al.: Skin lesion analysis toward melanoma detection: a challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE (2018). https:\/\/doi.org\/10.1109\/ISBI.2018.8363547","DOI":"10.1109\/ISBI.2018.8363547"},{"issue":"1","key":"10_CR8","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1186\/s12859-023-05516-5","volume":"24","author":"L Di Biasi","year":"2023","unstructured":"Di Biasi, L., De Marco, F., Auriemma Citarella, 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. BMC Bioinform. 24(1), 386 (2023)","journal-title":"BMC Bioinform."},{"key":"10_CR9","doi-asserted-by":"publisher","unstructured":"Germain, N., et al.: Stigma in visible skin diseases - a literature review and development of a conceptual model. J. Eur. Acad. Dermatology Venereol. 35(7) (2021). https:\/\/doi.org\/10.1111\/jdv.17110","DOI":"10.1111\/jdv.17110"},{"issue":"19","key":"10_CR10","doi-asserted-by":"publisher","first-page":"6578","DOI":"10.1016\/j.eswa.2015.04.034","volume":"42","author":"I Giotis","year":"2015","unstructured":"Giotis, I., Molders, N., Land, S., Biehl, M., Jonkman, M.F., Petkov, N.: MED-NODE: a computer-assisted melanoma diagnosis system using non-dermoscopic images. Expert Syst. Appl. 42(19), 6578\u20136585 (2015). https:\/\/doi.org\/10.1016\/j.eswa.2015.04.034","journal-title":"Expert Syst. Appl."},{"key":"10_CR11","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27 (2014)"},{"issue":"7","key":"10_CR12","doi-asserted-by":"publisher","first-page":"1529","DOI":"10.1016\/j.jid.2018.01.028","volume":"138","author":"SS Han","year":"2018","unstructured":"Han, S.S., Kim, M.S., Lim, W., Park, G.H., Park, I., Chang, S.E.: Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm. J. Investig. Dermatol. 138(7), 1529\u20131538 (2018). https:\/\/doi.org\/10.1016\/j.jid.2018.01.028","journal-title":"J. Investig. Dermatol."},{"key":"10_CR13","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"10_CR14","doi-asserted-by":"publisher","unstructured":"Heenaye-Mamode\u00a0Khan, M., et al.: Multi-class skin problem classification using deep generative adversarial network (DGAN). Computat. Intell. Neurosci. 2022, 1\u201313 (2022). https:\/\/doi.org\/10.1155\/2022\/1797471","DOI":"10.1155\/2022\/1797471"},{"key":"10_CR15","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local Nash equilibrium. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"10_CR16","doi-asserted-by":"publisher","unstructured":"Hou, B., Yan, R.: Convolutional auto-encoder based deep feature learning for finger-vein verification. In: 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1\u20135. IEEE Press (2018). https:\/\/doi.org\/10.1109\/MeMeA.2018.8438719","DOI":"10.1109\/MeMeA.2018.8438719"},{"key":"10_CR17","doi-asserted-by":"publisher","unstructured":"Kawahara, J., Daneshvar, S., Argenziano, G., Hamarneh, G.: Seven-point checklist and skin lesion classification using multitask multimodal neural nets. IEEE J. Biomed. Health Inform. 23(2), 538\u2013546 (2019). https:\/\/doi.org\/10.1109\/JBHI.2018.2824327. https:\/\/ieeexplore.ieee.org\/document\/8333693\/","DOI":"10.1109\/JBHI.2018.2824327"},{"key":"10_CR18","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1016\/j.neucom.2021.08.096","volume":"464","author":"H Li","year":"2021","unstructured":"Li, H., Pan, Y., Zhao, J., Zhang, L.: Skin disease diagnosis with deep learning: a review. Neurocomputing 464, 364\u2013393 (2021). https:\/\/doi.org\/10.1016\/j.neucom.2021.08.096","journal-title":"Neurocomputing"},{"issue":"1","key":"10_CR19","doi-asserted-by":"publisher","DOI":"10.1002\/ski2.81","volume":"2","author":"KA Muhaba","year":"2022","unstructured":"Muhaba, K.A., Dese, K., Aga, T.M., Zewdu, F.T., Simegn, G.L.: Automatic skin disease diagnosis using deep learning from clinical image and patient information. Skin Health Dis. 2(1), e81 (2022). https:\/\/doi.org\/10.1002\/ski2.81","journal-title":"Skin Health Dis."},{"issue":"1","key":"10_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-12646-y","volume":"12","author":"D Mukherkjee","year":"2022","unstructured":"Mukherkjee, D., Saha, P., Kaplun, D., Sinitca, A., Sarkar, R.: Brain tumor image generation using an aggregation of GAN models with style transfer. Sci. Rep. 12(1), 1\u201316 (2022)","journal-title":"Sci. Rep."},{"key":"10_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2023\/6370416","volume":"2023","author":"TD Nigat","year":"2023","unstructured":"Nigat, T.D., Sitote, T.M., Gedefaw, B.M.: Fungal skin disease classification using the convolutional neural network. J. Healthcare Eng. 2023, 1\u20139 (2023). https:\/\/doi.org\/10.1155\/2023\/6370416","journal-title":"J. Healthcare Eng."},{"key":"10_CR22","unstructured":"Paszke, A., et al.: Contributors: Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32, pp. 8024\u20138035 (2019)"},{"key":"10_CR23","doi-asserted-by":"publisher","DOI":"10.7860\/JCDR\/2016\/17209.7270","author":"AK Pathak","year":"2016","unstructured":"Pathak, A.K.: Study of drug utilization pattern for skin diseases in dermatology OPD of an Indian tertiary care hospital - a prescription survey. J. Clin. Diagn. Res. (2016). https:\/\/doi.org\/10.7860\/JCDR\/2016\/17209.7270","journal-title":"J. Clin. Diagn. Res."},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"10_CR25","unstructured":"Seitzer, M.: PyTorch-fid: FID Score for PyTorch (2020). https:\/\/github.com\/mseitzer\/pytorch-fid, version 0.3.0"},{"key":"10_CR26","doi-asserted-by":"publisher","unstructured":"Shaik, R., Bodhapati, S.K., Uddandam, A., Krupal, L., Sengupta, J.: A deep learning model that diagnosis skin diseases and recommends medication. In: 2022 1st International Conference on the Paradigm Shifts in Communication, Embedded Systems, Machine Learning and Signal Processing (PCEMS), Nagpur, India, pp. 7\u201310. IEEE (2022). https:\/\/doi.org\/10.1109\/PCEMS55161.2022.9808065","DOI":"10.1109\/PCEMS55161.2022.9808065"},{"key":"10_CR27","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"10_CR28","doi-asserted-by":"publisher","unstructured":"Sonawane, M.M., Gore, R.D., Gawali, B.W., Manza, R.R., Mendhekar, S.N.: Identification of skin disease using machine learning. In: Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022), pp. 99\u2013113. Atlantis Press International BV, Dordrecht (2023). https:\/\/doi.org\/10.2991\/978-94-6463-196-8_9","DOI":"10.2991\/978-94-6463-196-8_9"},{"key":"10_CR29","doi-asserted-by":"publisher","unstructured":"Sreekala, K., et al.: Skin diseases classification using hybrid AI based localization approach. Comput. Intell. Neurosci. 2022, 1\u20137 (2022). https:\/\/doi.org\/10.1155\/2022\/6138490","DOI":"10.1155\/2022\/6138490"},{"key":"10_CR30","unstructured":"Tan, M., Le, Q.: Efficientnetv2: smaller models and faster training. In: International Conference on Machine Learning, pp. 10096\u201310106. PMLR (2021)"},{"key":"10_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2021.110714","volume":"144","author":"M To\u011fa\u00e7ar","year":"2021","unstructured":"To\u011fa\u00e7ar, M., C\u00f6mert, Z., Ergen, B.: Intelligent skin cancer detection applying autoencoder, MobileNetV2 and spiking neural networks. Chaos Solitons Fractals 144, 110714 (2021). https:\/\/doi.org\/10.1016\/j.chaos.2021.110714","journal-title":"Chaos Solitons Fractals"},{"key":"10_CR32","doi-asserted-by":"publisher","unstructured":"Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning, ICML 2008, Helsinki, Finland, pp. 1096\u20131103. ACM Press (2008). https:\/\/doi.org\/10.1145\/1390156.1390294. http:\/\/portal.acm.org\/citation.cfm?doid=1390156.1390294","DOI":"10.1145\/1390156.1390294"},{"key":"10_CR33","doi-asserted-by":"publisher","unstructured":"Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol.\u00a01, p.\u00a0I (2001). https:\/\/doi.org\/10.1109\/CVPR.2001.990517","DOI":"10.1109\/CVPR.2001.990517"},{"key":"10_CR34","unstructured":"Yang, S., Xiao, W., Zhang, M., Guo, S., Zhao, J., Shen, F.: Image data augmentation for deep learning: a survey. arXiv preprint arXiv:2204.08610 (2022)"},{"key":"10_CR35","unstructured":"Yi, X., Walia, E., Babyn, P.: Unsupervised and semi-supervised learning with categorical generative adversarial networks assisted by Wasserstein distance for dermoscopy image classification. arXiv preprint arXiv:1804.03700 (2018)"},{"key":"10_CR36","unstructured":"Zhang, Y.: A better autoencoder for image: convolutional autoencoder. In: ICONIP17-DCEC (2018). http:\/\/users.cecs.anu.edu.au\/Tom.Gedeon\/conf\/ABCs2018\/paper\/ABCs2018_paper_58.pdf. Accessed 23 Mar 2017"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78201-5_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T16:02:53Z","timestamp":1733068973000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78201-5_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,2]]},"ISBN":["9783031782008","9783031782015"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78201-5_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,2]]},"assertion":[{"value":"2 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","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 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}