{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T19:29:39Z","timestamp":1781897379845,"version":"3.54.5"},"reference-count":146,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,1,2]],"date-time":"2021-01-02T00:00:00Z","timestamp":1609545600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministerio de Econom\u00eda, Industria y Competitividad (MINECO), Agencia Estatal de Investigaci\u00f3n (AEI)","award":["DPI2017-89414-R"],"award-info":[{"award-number":["DPI2017-89414-R"]}]},{"name":"European Regional Development Fund (FEDER)","award":["FIS2017-89850-R."],"award-info":[{"award-number":["FIS2017-89850-R."]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The worldwide incidence of skin cancer has risen rapidly in the last decades, becoming one in three cancers nowadays. Currently, a person has a 4% chance of developing melanoma, the most aggressive form of skin cancer, which causes the greatest number of deaths. In the context of increasing incidence and mortality, skin cancer bears a heavy health and economic burden. Nevertheless, the 5-year survival rate for people with skin cancer significantly improves if the disease is detected and treated early. Accordingly, large research efforts have been devoted to achieve early detection and better understanding of the disease, with the aim of reversing the progressive trend of rising incidence and mortality, especially regarding melanoma. This paper reviews a variety of the optical modalities that have been used in the last years in order to improve non-invasive diagnosis of skin cancer, including confocal microscopy, multispectral imaging, three-dimensional topography, optical coherence tomography, polarimetry, self-mixing interferometry, and machine learning algorithms. The basics of each of these technologies together with the most relevant achievements obtained are described, as well as some of the obstacles still to be resolved and milestones to be met.<\/jats:p>","DOI":"10.3390\/s21010252","type":"journal-article","created":{"date-parts":[[2021,1,3]],"date-time":"2021-01-03T19:54:46Z","timestamp":1609703686000},"page":"252","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":105,"title":["Optical Technologies for the Improvement of Skin Cancer Diagnosis: A Review"],"prefix":"10.3390","volume":"21","author":[{"given":"Laura","family":"Rey-Barroso","sequence":"first","affiliation":[{"name":"Centre for Sensors, Instruments and Systems Development, Universitat Polit\u00e8cnica de Catalunya, 08222 Terrassa, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sara","family":"Pe\u00f1a-Guti\u00e9rrez","sequence":"additional","affiliation":[{"name":"Centre for Sensors, Instruments and Systems Development, Universitat Polit\u00e8cnica de Catalunya, 08222 Terrassa, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4216-3794","authenticated-orcid":false,"given":"Carlos","family":"Y\u00e1\u00f1ez","sequence":"additional","affiliation":[{"name":"Centre for Sensors, Instruments and Systems Development, Universitat Polit\u00e8cnica de Catalunya, 08222 Terrassa, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3749-6850","authenticated-orcid":false,"given":"Francisco J.","family":"Burgos-Fern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Centre for Sensors, Instruments and Systems Development, Universitat Polit\u00e8cnica de Catalunya, 08222 Terrassa, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8166-1617","authenticated-orcid":false,"given":"Meritxell","family":"Vilaseca","sequence":"additional","affiliation":[{"name":"Centre for Sensors, Instruments and Systems Development, Universitat Polit\u00e8cnica de Catalunya, 08222 Terrassa, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0136-8301","authenticated-orcid":false,"given":"Santiago","family":"Royo","sequence":"additional","affiliation":[{"name":"Centre for Sensors, Instruments and Systems Development, Universitat Polit\u00e8cnica de Catalunya, 08222 Terrassa, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,2]]},"reference":[{"key":"ref_1","unstructured":"(2020, October 19). The Skin Cancer Foundation. Available online: https:\/\/www.skincancer.org\/."},{"key":"ref_2","unstructured":"Wolff, K., and Allen, J.R. (2009). Fitzpatrick\u2019s Color Atlas and Synopsis of Clinical Dermatology, McGraw-Hill Professional."},{"key":"ref_3","unstructured":"(2020, October 19). American Cancer Society. Available online: https:\/\/www.cancer.org."},{"key":"ref_4","unstructured":"World Health Organization (2020, October 19). Available online: https:\/\/www.who.int\/en\/."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1016\/j.jaad.2008.10.065","article-title":"Strategies for early melanoma detection: Approaches to the patient with nevi","volume":"60","author":"Grossman","year":"2009","journal-title":"J. Am. Acad. Dermatol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1016\/j.amepre.2012.07.031","article-title":"Melanoma Treatment Costs","volume":"43","author":"Guy","year":"2012","journal-title":"Am. J. Prev. Med."},{"key":"ref_7","unstructured":"Cancer Research, UK (2020, October 19). Available online: http:\/\/www.cancerresearchuk.org."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1007\/s13671-018-0218-9","article-title":"Confocal Microscopy in Skin Cancer","volume":"7","author":"Laimer","year":"2018","journal-title":"Curr. Derm. Rep."},{"key":"ref_9","unstructured":"Fellers, T.J., and Davidson, M.W. (2020, October 24). OLYMPUS Microscopy Resource Center. Available online: https:\/\/www.olympus-lifescience.com\/es\/microscope-resource\/primer\/techniques\/confocal\/confocalintro\/."},{"key":"ref_10","first-page":"1","article-title":"Advances in the use of reflectance confocal microscopy in melanoma","volume":"5","author":"Star","year":"2018","journal-title":"Melanoma Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/j.det.2016.05.012","article-title":"In Vivo and Ex Vivo Confocal Microscopy for Dermatologic and Mohs Surgeons","volume":"34","author":"Longo","year":"2016","journal-title":"Dermatol. Clin."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"061212","DOI":"10.1117\/1.JBO.18.6.061212","article-title":"In vivo confocal microscopy in dermatology: From research to clinical application","volume":"18","author":"Ulrich","year":"2013","journal-title":"J. Biomed. Opt."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wilhelm, K.P., Elsner, P., Berardesca, E., and Maibach, H.I. (2006). Confocal microscopy of skin in vitro and ex vivo. Bioengineering of the Skin: Skin Imaging and Analysis, CRC Press Taylor & Francis Group.","DOI":"10.3109\/9781420005516"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1038\/modpathol.3800330","article-title":"In vivo assessment of melanocytic nests in nevi and melanomas by reflectance confocal microscopy","volume":"18","author":"Pellacani","year":"2005","journal-title":"Mod. Pathol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1038\/jid.2008.193","article-title":"In Vivo Reflectance Confocal Microscopy Enhances Secondary Evaluation of Melanocytic Lesions","volume":"129","author":"Guitera","year":"2009","journal-title":"J. Investig. Dermatol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2386","DOI":"10.1038\/jid.2012.172","article-title":"In Vivo Confocal Microscopy for Diagnosis of Melanoma and Basal Cell Carcinoma Using a Two-Step Method: Analysis of 710 Consecutive Clinically Equivocal Cases","volume":"132","author":"Guitera","year":"2012","journal-title":"J. Investig. Dermatol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.jaad.2009.02.014","article-title":"Development of a two-step method for the diagnosis of melanoma by reflectance confocal microscopy","volume":"61","author":"Segura","year":"2009","journal-title":"J. Am. Acad. Dermatol."},{"key":"ref_18","first-page":"610","article-title":"Clinical applicability of in vivo reflectance confocal microscopy for the diagnosis of actinic keratoses","volume":"34","author":"Ulrich","year":"2008","journal-title":"Dermatol. Surg."},{"key":"ref_19","first-page":"620","article-title":"Discrimination of actinic keratoses from normal skin with reflectance mode confocal microscopy","volume":"34","author":"Horn","year":"2008","journal-title":"Dermatol. Surg."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"054001","DOI":"10.1117\/1.2981828","article-title":"Confocal mosaicing microscopy in Mohs skin excisions: Feasibility of rapid surgical pathology","volume":"13","author":"Gareau","year":"2008","journal-title":"J. Biomed. Opt."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"034012","DOI":"10.1117\/1.3130331","article-title":"Sensitivity and specificity for detecting basal cell carcinomas in Mohs excisions with confocal fluorescence mosaicing microscopy","volume":"14","author":"Gareau","year":"2009","journal-title":"J. Biomed. Opt."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"050504","DOI":"10.1117\/1.3582335","article-title":"Rapid confocal imaging of large areas of excised tissue with strip mosaicing","volume":"16","author":"Abeytunge","year":"2011","journal-title":"J. Biomed. Opt."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"100901","DOI":"10.1117\/1.JBO.18.10.100901","article-title":"Review of spectral imaging technology in biomedical engineering: Achievements and challenges","volume":"18","author":"Li","year":"2013","journal-title":"J. Biomed. Opt."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Grahn, H.F., and Geladi, P. (2007). Techniques and Applications of Hyperspectral Image Analysis, John Wiley & Sons, Ltd.","DOI":"10.1002\/9780470010884"},{"key":"ref_25","unstructured":"Society of Photo Optical (2006). Computational Color Technology, SPIE."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.infrared.2014.09.017","article-title":"Dynamic infrared imaging for skin cancer screening","volume":"70","author":"Godoy","year":"2015","journal-title":"Infrared Phys. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Emery, J.D., Hunter, J., Hall, P.N., Watson, A.J., Moncrieff, M., and Walter, F.M. (2010). Accuracy of SIAscopy for pigmented skin lesions encountered in primary care: Development and validation of a new diagnostic algorithm. BMC Dermatol., 10.","DOI":"10.1186\/1471-5945-10-9"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"065006","DOI":"10.1117\/1.JBO.22.6.065006","article-title":"Multispectral imaging system based on light-emitting diodes for the detection of melanomas and basal cell carcinomas: A pilot study","volume":"22","author":"Delpueyo","year":"2017","journal-title":"J. Biomed. Opt."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1001\/archdermatol.2010.302","article-title":"The performance of MelaFind: A prospective multicenter study","volume":"147","author":"Monheit","year":"2011","journal-title":"Arch. Dermatol."},{"key":"ref_30","first-page":"4","article-title":"Multispectral assessment of skin malformations using a modified video-microscope","volume":"49","author":"Bekina","year":"2012","journal-title":"Latv. J. Phys. Tech. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4826","DOI":"10.1364\/OE.21.004826","article-title":"Spectral morphological analysis of skin lesions with a polarization multispectral dermoscope","volume":"21","author":"Kapsokalyvas","year":"2013","journal-title":"Opt. Express"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2946","DOI":"10.1364\/BOE.8.002946","article-title":"Optical detection and monitoring of pigmented skin lesions","volume":"8","author":"Stamnes","year":"2017","journal-title":"Biomed. Opt. Express"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"060502","DOI":"10.1117\/1.3584846","article-title":"Towards noncontact skin melanoma selection by multispectral imaging analysis","volume":"16","author":"Kuzmina","year":"2011","journal-title":"J. Biomed. Opt."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"978289","DOI":"10.1155\/2013\/978289","article-title":"Skin parameter map retrieval from a dedicated multispectral imaging system applied to dermatology\/cosmetology","volume":"2013","author":"Jolivot","year":"2013","journal-title":"Int. J. Biomed. Imaging"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1088\/0031-9155\/45\/5\/312","article-title":"Multispectral imaging approach in the diagnosis of cutaneous melanoma: Potentiality and limits","volume":"45","author":"Farina","year":"2000","journal-title":"Phys. Med. Biol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1118\/1.1538230","article-title":"Automated melanoma detection: Multispectral imaging and neural network approach for classification","volume":"30","author":"Tomatis","year":"2003","journal-title":"Med. Phys."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Fioravanti, V., Brandhoff, L., van den Driesche, S., Breiteneder, H., Kitzw\u00f6gerer, M., Hafner, C., and Vellekoop, M. (2016). An Infrared Absorbance Sensor for the Detection of Melanoma in Skin Biopsies. Sensors, 16.","DOI":"10.3390\/s16101659"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2599","DOI":"10.1088\/0031-9155\/52\/9\/018","article-title":"Multispectral imaging and artificial neural network: Mimicking the management decision of the clinician facing pigmented skin lesions","volume":"52","author":"Carrara","year":"2007","journal-title":"Phys. Med. Biol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1364\/BOE.3.000467","article-title":"Clinical evaluation of melanomas and common nevi by spectral imaging","volume":"3","author":"Diebele","year":"2012","journal-title":"Biomed. Opt. Express"},{"key":"ref_40","first-page":"88030C","article-title":"Evaluation of skin melanoma in spectral range 450\u2013950 nm using principal component analysis","volume":"Volume 8803","author":"Lilge","year":"2013","journal-title":"Medical Laser Applications and Laser-Tissue Interactions VI"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5294","DOI":"10.1364\/BOE.7.005294","article-title":"Smartphone-based multispectral imaging: System development and potential for mobile skin diagnosis","volume":"7","author":"Kim","year":"2016","journal-title":"Biomed. Opt. Express"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1111\/his.13070","article-title":"Multispectral imaging for highly accurate analysis of tumour-infiltrating lymphocytes in primary melanoma","volume":"70","author":"Vasaturo","year":"2017","journal-title":"Histopathology"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Rey-Barroso, L., Burgos-Fern\u00e1ndez, F., Delpueyo, X., Ares, M., Royo, S., Malvehy, J., Puig, S., and Vilaseca, M. (2018). Visible and Extended Near-Infrared Multispectral Imaging for Skin Cancer Diagnosis. Sensors, 18.","DOI":"10.3390\/s18051441"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1111\/j.1365-4362.1974.tb05068.x","article-title":"New methods for surface ultrastructure: Comparative studies of scanning electron microscopy, transmission electron microscopy and replica method","volume":"13","author":"Hashimoto","year":"1974","journal-title":"Int. J. Dermatol."},{"key":"ref_45","first-page":"756","article-title":"Optical Metrology of Diffuse Surfaces","volume":"Volume 1","author":"Hocken","year":"2006","journal-title":"Optical Shop Testing"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Campolo, D. (2010). Skin Roughness Assessment. New Developments in Biomedical Engineering, InTech.","DOI":"10.5772\/154"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1111\/srt.12009","article-title":"Comparison of two in vivo measurements for skin surface topography","volume":"19","author":"Kottner","year":"2013","journal-title":"Skin Res. Technol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1034\/j.1600-0846.2002.00351.x","article-title":"Effect of exposure of human skin to a dry environment","volume":"8","author":"Egawa","year":"2002","journal-title":"Skin Res. Technol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1034\/j.1600-0846.2001.70303.x","article-title":"Irregularity skin index (ISI): A tool to evaluate skin surface texture","volume":"7","author":"Setaro","year":"2001","journal-title":"Skin Res. Technol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1034\/j.1600-0625.2003.00008.x","article-title":"Topical ascorbic acid on photoaged skin. Clinical, topographical and ultrastructural evaluation: Double-blind study vs. placebo","volume":"12","author":"Humbert","year":"2003","journal-title":"Exp. Dermatol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1111\/j.1600-0846.1995.tb00041.x","article-title":"Quantitative surface topography as a tool in the differential diagnosis between melanoma and naevus","volume":"1","author":"Connemann","year":"1995","journal-title":"Skin Res. Technol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1111\/j.1600-0846.2007.00246.x","article-title":"Influence of polyol and oil concentration in cosmetic products on skin moisturization and skin surface roughness","volume":"13","author":"Kim","year":"2007","journal-title":"Skin Res. Technol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1111\/j.1600-0846.2008.00300.x","article-title":"Non-invasive evaluation techniques to quantify the efficacy of cosmetic anti-cellulite products","volume":"14","author":"Bielfeldt","year":"2008","journal-title":"Skin Res. Technol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1034\/j.1600-0846.2003.00043.x","article-title":"SkinChip\u00ae, a new tool for investigating the skin surface in vivo","volume":"9","author":"Querleux","year":"2003","journal-title":"Skin Res. Technol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1111\/j.1600-0846.2007.00257.x","article-title":"Comparison between ultrasonography (Dermascan C version 3) and transparency profilometry (Skin Visiometer SV600)","volume":"14","author":"Lee","year":"2008","journal-title":"Skin Res. Technol."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Lotay, A.S., Carvalho, M.T., and Girkin, J.M. (2016). Non-invasive assessment of skin roughness through speckle pattern analysis. Proceedings of the Biomedical Optics 2016, Hollywood, FL, USA, 3\u20136 April 2016, OSA. Optics InfoBase Conference Papers.","DOI":"10.1364\/CANCER.2016.JTu3A.6"},{"key":"ref_57","unstructured":"Kollias, N., Choi, B., Zeng, H., Malek, R.S., Wong, B.J., Ilgner, J.F.R., Gregory, K.W., Tearney, G.J., Marcu, L., and Hirschberg, H. (2009). Optical discrimination of surface reflection from volume backscattering in speckle contrast for skin roughness measurements. Proceedings of the Photonic Therapeutics and Diagnostics V, San Jose, CA, USA, 19 February 2009, SPIE."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1111\/j.1600-0846.1999.tb00131.x","article-title":"Rapid in vivo measurement of the topography of human skin by active image triangulation using a digital micromirror device","volume":"5","author":"Jaspers","year":"1999","journal-title":"Skin Res. Technol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"60810D","DOI":"10.1117\/12.649516","article-title":"A binocular machine vision system for non-melanoma skin cancer 3D reconstruction","volume":"Volume 6081","author":"Azar","year":"2006","journal-title":"Multimodal Biomedical Imaging"},{"key":"ref_60","first-page":"1","article-title":"Three-dimensional imaging of normal skin and nonmelanoma skin cancer with cellular resolution using Gabor domain optical coherence microscopy","volume":"17","author":"Lee","year":"2012","journal-title":"J. Biomed. Opt."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1034\/j.1600-0846.2001.70210.x","article-title":"Skin topography measurement by interference fringe projection: A technical validation","volume":"7","author":"Lagarde","year":"2001","journal-title":"Skin Res. Technol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1111\/j.1600-0846.2005.00096.x","article-title":"Topography and anisotropy of the skin surface with ageing","volume":"11","author":"Lagarde","year":"2005","journal-title":"Skin Res. Technol."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Moore, C.J., Burton, D.R., Skydan, O., Sharrock, P.J., and Lalor, M. (2006). 3D Body Surface Measurement and Display in Radiotherapy Part I: Technology of Structured Light Surface Sensing. Proceedings of the International Conference on Medical Information Visualisation\u2014BioMedical Visualisation (MedVis\u201906), London, UK, 5\u20137 July 2006, IEEE.","DOI":"10.1109\/MEDIVIS.2006.3"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Ares, M., Royo, S., Vilaseca, M., Herrera, J.A., Delpueyo, X., and Sanabria, F. (2014). Handheld 3D Scanning System for In-Vivo Imaging of Skin Cancer. Proceedings of the 5th International Conference on 3D Body Scanning Technologies, Lugano, Switzerland, 21\u201322 October 2014, Hometrica Consulting-Dr. Nicola D\u2019Apuzzo.","DOI":"10.15221\/14.231"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"3404","DOI":"10.1364\/BOE.10.003404","article-title":"Morphological study of skin cancer lesions through a 3D scanner based on fringe projection and machine learning","volume":"10","author":"Ares","year":"2019","journal-title":"Biomed. Opt. Express"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1159\/000450760","article-title":"Skin Surface Topography and Texture Analysis of Sun-Exposed Body Sites in View of Sunscreen Application","volume":"29","author":"Korn","year":"2017","journal-title":"Skin Pharmacol. Physiol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"6157","DOI":"10.1364\/AO.31.006157","article-title":"Moir\u00e9 topography by slit beam scanning","volume":"31","author":"Kim","year":"1992","journal-title":"Appl. Opt."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"5760","DOI":"10.1364\/OPEX.12.005760","article-title":"Optical coherence tomography of skin for measurement of epidermal thickness by shapelet-based image analysis","volume":"12","author":"Weissman","year":"2004","journal-title":"Opt. Express"},{"key":"ref_69","unstructured":"Villiger, M.L., and Bouma, B.E. (2020, October 24). Center for Biomedical Oct Research & Translation-Working Principle of OCT. Available online: https:\/\/octresearch.org\/the-center\/oct-and-biomedical-optics\/working-principle-of-oct\/."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.sder.2009.07.002","article-title":"Optical coherence tomography for imaging of skin and skin diseases","volume":"28","author":"Mogensen","year":"2009","journal-title":"Semin. Cutan. Med. Surg."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.det.2017.06.008","article-title":"Optical Coherence Tomography in the Diagnosis of Skin Cancer","volume":"35","author":"Levine","year":"2017","journal-title":"Dermatol. Clin."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Ferrante di Ruffano, L., Dinnes, J., Deeks, J.J., Chuchu, N., Bayliss, S.E., Davenport, C., Takwoingi, Y., Godfrey, K., O\u2019Sullivan, C., and Matin, R.N. (2018). Optical coherence tomography for diagnosing skin cancer in adults. Cochrane Database Syst. Rev.","DOI":"10.1002\/14651858.CD013189"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1167\/iovs.16-20969","article-title":"Comparison between spectral-domain and swept-source optical coherence tomography angiographic imaging of choroidal neovascularization","volume":"58","author":"Miller","year":"2017","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/j.oftal.2016.03.004","article-title":"Swept Source OCT versus Spectral Domain OCT: Myths and realities","volume":"91","year":"2016","journal-title":"Arch. la Soc. Espa\u00f1ola Oftalmol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1159\/000444706","article-title":"Dynamic Optical Coherence Tomography in Dermatology","volume":"232","author":"Ulrich","year":"2016","journal-title":"Dermatology"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JBO.23.4.040901","article-title":"Advances in optical coherence tomography in dermatology\u2014a review","volume":"23","author":"Olsen","year":"2018","journal-title":"J. Biomed. Opt."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1002\/lsm.22788","article-title":"Optical coherence tomography angiography of normal skin and inflammatory dermatologic conditions","volume":"50","author":"Deegan","year":"2018","journal-title":"Lasers Surg. Med."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"036010","DOI":"10.1117\/1.JBO.19.3.036010","article-title":"Improved microcirculation imaging of human skin in vivo using optical microangiography with a correlation mapping mask","volume":"19","author":"Choi","year":"2014","journal-title":"J. Biomed. Opt."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"653","DOI":"10.2217\/iim.11.62","article-title":"3D optical coherence tomography for clinical diagnosis of nonmelanoma skin cancers","volume":"3","author":"Alex","year":"2011","journal-title":"Imaging Med."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1002\/lsm.20573","article-title":"Using optical coherence tomography to evaluate skin sun damage and precancer","volume":"39","author":"Korde","year":"2007","journal-title":"Lasers Surg. Med."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.mvr.2016.05.004","article-title":"Validation of Dynamic optical coherence tomography for non-invasive, in vivo microcirculation imaging of the skin","volume":"107","author":"Themstrup","year":"2016","journal-title":"Microvasc. Res."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JBO.23.2.020902","article-title":"Optical coherence tomography for the diagnosis of malignant skin tumors: A meta-analysis","volume":"23","author":"Xiong","year":"2018","journal-title":"J. Biomed. Opt."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Perchoux, J., Quotb, A., Atashkhooei, R., Azcona, F., Ram\u00edrez-Miquet, E., Bernal, O., Jha, A., Luna-Arriaga, A., Yanez, C., and Caum, J. (2016). Current Developments on Optical Feedback Interferometry as an All-Optical Sensor for Biomedical Applications. Sensors, 16.","DOI":"10.3390\/s16050694"},{"key":"ref_84","first-page":"91990M","article-title":"THz QCL self-mixing interferometry for biomedical applications","volume":"Volume 9199","author":"Razeghi","year":"2014","journal-title":"Terahertz Emitters, Receivers, and Applications V"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"5844","DOI":"10.1364\/AO.31.005844","article-title":"Self-mixing laser-Doppler velocimetry of liquid flow and of blood perfusion in tissue","volume":"31","author":"Koelink","year":"1992","journal-title":"Appl. Opt."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"75720A","DOI":"10.1117\/12.840240","article-title":"Blood flow measurement in extracorporeal circulation using self-mixing laser diode","volume":"Volume 7572","year":"2010","journal-title":"Optical Diagnostics and Sensing X: Toward Point-of-Care Diagnostics"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1109\/JSEN.2011.2131646","article-title":"Self-Mixing Laser Doppler Spectra of Extracorporeal Blood Flow: A Theoretical and Experimental Study","volume":"12","author":"Norgia","year":"2012","journal-title":"IEEE Sens. J."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Shen, X., Zhang, M., Yu, J., Li, J., Wang, X., Perchoux, J., Moreira, R.D.C., and Chen, T. (2020). Self-Mixing Interferometry-Based Micro Flow Cytometry System for Label-Free Cells Classification. Appl. Sci., 10.","DOI":"10.3390\/app10020478"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1364\/OL.24.000744","article-title":"Laser optical feedback tomography","volume":"24","author":"Lacot","year":"1999","journal-title":"Opt. Lett."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"4037","DOI":"10.1364\/BOE.8.004037","article-title":"Confocal laser feedback tomography for skin cancer detection","volume":"8","author":"Mowla","year":"2017","journal-title":"Biomed. Opt. Express"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"24340","DOI":"10.1364\/OE.27.024340","article-title":"Confocal flowmeter based on self-mixing interferometry for real-time velocity profiling of turbid liquids flowing in microcapillaries","volume":"27","author":"Azcona","year":"2019","journal-title":"Opt. Express"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.1007\/s10103-019-02752-1","article-title":"Polarization-resolved Stokes-Mueller imaging: A review of technology and applications","volume":"34","author":"Spandana","year":"2019","journal-title":"Lasers Med. Sci."},{"key":"ref_93","first-page":"381","article-title":"Polarized multispectral imaging for the diagnosis of skin cancer","volume":"2019","author":"Royo","year":"2019","journal-title":"Final Progr. Proc.-IS T\/SID Color Imaging Conf."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JBO.23.12.125004","article-title":"Degree of optical polarization as a tool for detecting melanoma: Proof of principle","volume":"23","author":"Louie","year":"2018","journal-title":"J. Biomed. Opt."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"4933","DOI":"10.1364\/BOE.8.004933","article-title":"Real time complete Stokes polarimetric imager based on a linear polarizer array camera for tissue polarimetric imaging","volume":"8","author":"Qi","year":"2017","journal-title":"Biomed. Opt. Express"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Mazumder, N., and Kao, F.J. (2020). Stokes polarimetry-based second harmonic generation microscopy for collagen and skeletal muscle fiber characterization. Lasers Med. Sci.","DOI":"10.1007\/s10103-020-03144-6"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"1106","DOI":"10.1364\/JOSAA.13.001106","article-title":"Interpretation of Mueller matrices based on polar decomposition","volume":"13","author":"Lu","year":"1996","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1364\/OE.20.001151","article-title":"Experimental validation of Mueller matrix differential decomposition","volume":"20","author":"Ossikovski","year":"2012","journal-title":"Opt. Express"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"061104","DOI":"10.1117\/1.JBO.20.6.061104","article-title":"Polarized light imaging in biomedicine: Emerging Mueller matrix methodologies for bulk tissue assessment","volume":"20","author":"Alali","year":"2015","journal-title":"J. Biomed. Opt."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1364\/AO.377105","article-title":"Assessment of anisotropy of collagen structures through spatial frequencies of Mueller matrix images for cervical pre-cancer detection","volume":"59","author":"Zaffar","year":"2020","journal-title":"Appl. Opt."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"241103","DOI":"10.1063\/1.4811414","article-title":"The origins of polarimetric image contrast between healthy and cancerous human colon tissue","volume":"102","author":"Novikova","year":"2013","journal-title":"Appl. Phys. Lett."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"101708","DOI":"10.1016\/j.pdpdt.2020.101708","article-title":"Mueller matrix polarimetry for characterization of skin tissue samples: A review","volume":"30","author":"Ahmad","year":"2020","journal-title":"Photodiagnosis Photodyn. Ther."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"0760141","DOI":"10.1117\/1.JBO.17.7.076014","article-title":"Out-of-plane Stokes imaging polarimeter for early skin cancer diagnosis","volume":"17","author":"Ghassemi","year":"2012","journal-title":"J. Biomed. Opt."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Fricke, D., Maas, S., J\u00fctte, L., Wollweber, M., and Roth, B. (2019). Non-Contact Fast Mueller Matrix Measurement System for Investigation of Inflammatory Skin Diseases, SPIE.","DOI":"10.1117\/12.2509766"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1111\/srt.12713","article-title":"The role of AI classifiers in skin cancer images","volume":"25","author":"Magalhaes","year":"2019","journal-title":"Skin Res. Technol."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2342-14-36","article-title":"Automatic diagnosis of melanoma using machine learning methods on a spectroscopic system","volume":"14","author":"Li","year":"2014","journal-title":"BMC Med. Imaging"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"3721","DOI":"10.1364\/BOE.7.003721","article-title":"Classification of basal cell carcinoma in human skin using machine learning and quantitative features captured by polarization sensitive optical coherence tomography","volume":"7","author":"Marvdashti","year":"2016","journal-title":"Biomed. Opt. Express"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.3844\/ajassp.2011.1159.1168","article-title":"Dermoscopic Image Segmentation using Machine Learning Algorithm","volume":"8","author":"Suresh","year":"2011","journal-title":"Am. J. Appl. Sci."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"3713","DOI":"10.1007\/s11042-018-6927-z","article-title":"Classification of melanoma from Dermoscopic data using machine learning techniques","volume":"79","author":"Janney","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1006\/jbin.2001.1004","article-title":"A comparison of machine learning methods for the diagnosis of pigmented skin lesions","volume":"34","author":"Dreiseitl","year":"2001","journal-title":"J. Biomed. Inform."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"2187","DOI":"10.1016\/j.patrec.2011.06.015","article-title":"Toward a combined tool to assist dermatologists in melanoma detection from dermoscopic images of pigmented skin lesions","volume":"32","author":"Capdehourat","year":"2011","journal-title":"Pattern Recognit. Lett."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"181","DOI":"10.18280\/ts.370204","article-title":"Detection of skin cancer image by feature selection methods using new buzzard optimization (BUZO) algorithm","volume":"37","author":"Arshaghi","year":"2020","journal-title":"Trait. du Signal"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.compmedimag.2015.02.011","article-title":"Automatic differentiation of melanoma from dysplastic nevi","volume":"43","author":"Rastgoo","year":"2015","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"e2953","DOI":"10.1002\/cnm.2953","article-title":"Machine learning\u2013based diagnosis of melanoma using macro images","volume":"34","author":"Gautam","year":"2018","journal-title":"Int. J. Numer. Method. Biomed. Eng."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1002\/ima.20261","article-title":"Learning methods for melanoma recognition","volume":"20","author":"Torre","year":"2010","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1036","DOI":"10.1109\/TMI.2015.2506270","article-title":"Machine Learning Methods for Binary and Multiclass Classification of Melanoma Thickness from Dermoscopic Images","volume":"35","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.knosys.2018.05.016","article-title":"Dermoscopic assisted diagnosis in melanoma: Reviewing results, optimizing methodologies and quantifying empirical guidelines","volume":"158","author":"Lee","year":"2018","journal-title":"Knowl. -Based Syst."},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Hameed, N., Hameed, F., Shabut, A., Khan, S., Cirstea, S., and Hossain, A. (2019). An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions. Computers, 8.","DOI":"10.3390\/computers8030062"},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Barros, W.K.P., Morais, D.S., Lopes, F.F., Torquato, M.F., Barbosa, R.d.M., and Fernandes, M.A.C. (2020). Proposal of the CAD system for melanoma detection using reconfigurable computing. Sensors, 20.","DOI":"10.3390\/s20113168"},{"key":"ref_120","first-page":"65","article-title":"A device for the color measurement and detection of spots on the skin. 2008, 14, 65\u201370","volume":"14","author":"Pujol","year":"2008","journal-title":"Skin Res. Technol."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/72.788640","article-title":"An overview of statistical learning theory","volume":"10","author":"Vapnik","year":"1999","journal-title":"IEEE Trans. Neural Networks"},{"key":"ref_122","unstructured":"Dasarathy, B. (1991). V Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques, IEEE Comput. Soc. Press."},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Bishop, C.M. (1996). Neural Networks: A Pattern Recognition Perspective, Oxford University Press. [2nd ed.].","DOI":"10.1201\/9781420050646.ptb6"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/S0020-7373(87)80053-6","article-title":"Simplifying decision trees","volume":"27","author":"Quinlan","year":"1987","journal-title":"Int. J. Man. Mach. Stud."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"1876","DOI":"10.1109\/TMI.2017.2695227","article-title":"Deep Fully Convolutional Networks With Jaccard Distance","volume":"36","author":"Yuan","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Li, Y., and Shen, L. (2018). Skin lesion analysis towards melanoma detection using deep learning network. Sensors, 18.","DOI":"10.3390\/s18020556"},{"key":"ref_128","first-page":"409","article-title":"Classification of melanoma skin cancer using convolutional neural network","volume":"10","author":"Refianti","year":"2019","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"15635","DOI":"10.1007\/s11042-019-07814-8","article-title":"Improving detection of Melanoma and Naevus with deep neural networks","volume":"79","author":"Maiti","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1007\/s10916-016-0460-2","article-title":"Novel Approaches for Diagnosing Melanoma Skin Lesions through Supervised and Deep Learning Algorithms","volume":"40","author":"Premaladha","year":"2016","journal-title":"J. Med. Syst."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.compmedimag.2018.10.007","article-title":"Fusing fine-tuned deep features for skin lesion classification","volume":"71","author":"Mahbod","year":"2019","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1007\/s10916-019-1413-3","article-title":"Region Extraction and Classification of Skin Cancer: A Heterogeneous framework of Deep CNN Features Fusion and Reduction","volume":"43","author":"Saba","year":"2019","journal-title":"J. Med. Syst."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.patrec.2020.05.019","article-title":"A new approach for classification skin lesion based on transfer learning, deep learning, and IoT system","volume":"136","author":"Rodrigues","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_136","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very deep convolutional networks for large-scale image recognition. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015-Conference Track Proceedings, San Diego, CA, USA."},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 17\u201319). Going deeper with convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"105475","DOI":"10.1016\/j.cmpb.2020.105475","article-title":"Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification","volume":"193","author":"Mahbod","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"1006","DOI":"10.1109\/TBME.2018.2866166","article-title":"Melanoma Recognition in Dermoscopy Images via Aggregated Deep Convolutional Features","volume":"66","author":"Yu","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"105725","DOI":"10.1016\/j.asoc.2019.105725","article-title":"Intelligent skin cancer diagnosis using improved particle swarm optimization and deep learning models","volume":"84","author":"Tan","year":"2019","journal-title":"Appl. Soft Comput. J."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.jbi.2018.08.006","article-title":"Skin lesion classification with ensembles of deep convolutional neural networks","volume":"86","author":"Harangi","year":"2018","journal-title":"J. Biomed. Inform."},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Olivas, E.S., Guerrero, J.D.M., Martinez Sober, M., Magdalena Benedito, J.R., and Serrano L\u00f3pez, A.J. (2009). Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, IGI Global.","DOI":"10.4018\/978-1-60566-766-9"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"31254","DOI":"10.1109\/ACCESS.2020.2973188","article-title":"Deep Learning from Limited Training Data: Novel Segmentation and Ensemble Algorithms Applied to Automatic Melanoma Diagnosis","volume":"8","author":"Albert","year":"2020","journal-title":"IEEE Access"},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Zhang, X., Wang, S., Liu, J., and Tao, C. (2018). Towards improving diagnosis of skin diseases by combining deep neural network and human knowledge. BMC Med. Inform. Decis. Mak., 18.","DOI":"10.1186\/s12911-018-0631-9"},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"105568","DOI":"10.1016\/j.cmpb.2020.105568","article-title":"A GAN-based image synthesis method for skin lesion classification","volume":"195","author":"Qin","year":"2020","journal-title":"Comput. Methods Programs Biomed."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/1\/252\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:06:24Z","timestamp":1760159184000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/1\/252"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,2]]},"references-count":146,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["s21010252"],"URL":"https:\/\/doi.org\/10.3390\/s21010252","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,2]]}}}