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BCC and SCC, while constituting the most prevalent categories of skin cancer, are generally considered less aggressive compared to MM. Notably, MM possesses a greater capacity for invasiveness, enabling infiltration into adjacent tissues and dissemination via both the circulatory and lymphatic systems. Risk factors associated with skin cancer encompass ultraviolet (UV) radiation exposure, fair skin complexion, a history of sunburn incidents, genetic predisposition, immunosuppressive conditions, and exposure to environmental carcinogens. Early detection of skin cancer is of paramount importance to optimize treatment outcomes and preclude the progression of disease, either locally or to distant sites. In pursuit of this objective, numerous computer-aided diagnosis (CAD) systems have been developed. Hyperspectral imaging (HSI), distinguished by its capacity to capture information spanning the electromagnetic spectrum, surpasses conventional RGB imaging, which relies solely on three color channels. Consequently, this study offers a comprehensive exploration of recent CAD investigations pertaining to skin cancer detection and diagnosis utilizing HSI, emphasizing diagnostic performance parameters such as sensitivity and specificity.<\/jats:p>","DOI":"10.3390\/cancers15235634","type":"journal-article","created":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T12:01:00Z","timestamp":1701259260000},"page":"5634","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["A Review of Recent Advances in Computer-Aided Detection Methods Using Hyperspectral Imaging Engineering to Detect Skin Cancer"],"prefix":"10.3390","volume":"15","author":[{"given":"Hung-Yi","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Dermatology, Ditmanson Medical Foundation Chiayi Christian Hospital, Chia Yi City 60002, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Ping","family":"Hsiao","sequence":"additional","affiliation":[{"name":"Department of Dermatology, Chung Shan Medical University Hospital, No.110, Sec. 1, Jianguo N. Rd., South District, Taichung City 40201, Taiwan"},{"name":"Institute of Medicine, School of Medicine, Chung Shan Medical University, No.110, Sec. 1, Jianguo N. Rd., South District, Taichung City 40201, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Riya","family":"Karmakar","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi City 62102, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7741-3722","authenticated-orcid":false,"given":"Arvind","family":"Mukundan","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi City 62102, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pramod","family":"Chaudhary","sequence":"additional","affiliation":[{"name":"Department of Aeronautical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai 600 062, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shang-Chin","family":"Hsieh","sequence":"additional","affiliation":[{"name":"Department of Plastic Surgery, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st. Rd., Lingya District, Kaohsiung 80284, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4107-2062","authenticated-orcid":false,"given":"Hsiang-Chen","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi City 62102, Taiwan"},{"name":"Department of Medical Research, Dalin Tzu Chi General Hospital, No. 2, Min-Sheng Rd., Dalin Town, Chia Yi City 62247, Taiwan"},{"name":"Technology Development, Hitspectra Intelligent Technology Co., Ltd., Kaohsiung 80661, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,29]]},"reference":[{"key":"ref_1","first-page":"5257","article-title":"Skin cancer concerns in people of color: Risk factors and prevention","volume":"17","author":"Gupta","year":"2016","journal-title":"Asian Pac. J. Cancer Prev. APJCP"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA A Cancer J. Clin."},{"key":"ref_3","unstructured":"Leiter, U., Keim, U., and Garbe, C. (2020). Sunlight, Vitamin D and Skin Cancer, Springer."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Fahradyan, A., Howell, A.C., Wolfswinkel, E.M., Tsuha, M., Sheth, P., and Wong, A.K. (2017). Updates on the management of non-melanoma skin cancer (NMSC). Healthcare, 5.","DOI":"10.3390\/healthcare5040082"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1941","DOI":"10.1002\/ijc.31937","article-title":"Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods","volume":"144","author":"Ferlay","year":"2019","journal-title":"Int. J. Cancer"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"10","DOI":"10.3389\/fphy.2022.821057","article-title":"A review on laser-induced breakdown spectroscopy in different cancers diagnosis and classification","volume":"10","author":"Khan","year":"2022","journal-title":"Front. Phys."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1007\/s00403-018-1834-8","article-title":"Characterization and survival of patients with metastatic basal cell carcinoma in the Department of Veterans Affairs: A retrospective electronic health record review","volume":"310","author":"Stevens","year":"2018","journal-title":"Arch. Dermatol. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1097\/DSS.0000000000001276","article-title":"Correlation between original biopsy pathology and Mohs intraoperative pathology","volume":"44","author":"Stiegel","year":"2018","journal-title":"Dermatol. Surg."},{"key":"ref_9","first-page":"e13","article-title":"Global incidence and mortality of skin cancer by histological subtype and its relationship with the human development index (HDI)\u2014An ecology study in 2018","volume":"6","author":"Khazaei","year":"2019","journal-title":"World Cancer Res. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4337","DOI":"10.1038\/s41598-021-83502-8","article-title":"The incidence and clinical analysis of non-melanoma skin cancer","volume":"11","author":"Lange","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Popescu, D., El-Khatib, M., El-Khatib, H., and Ichim, L. (2022). New Trends in Melanoma Detection Using Neural Networks: A Systematic Review. Sensors, 22.","DOI":"10.3390\/s22020496"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"819","DOI":"10.2217\/fon.12.84","article-title":"New diagnostics for melanoma detection: From artificial intelligence to RNA microarrays","volume":"8","author":"Laimer","year":"2012","journal-title":"Future Oncol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1111\/j.1365-4632.2010.04474.x","article-title":"Ultraviolet radiation and skin cancer","volume":"49","author":"Narayanan","year":"2010","journal-title":"Int. J. Dermatol."},{"key":"ref_14","unstructured":"Federico, M.B. (2020). Actioning the Global Goals for Local Impact, Springer."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"866733","DOI":"10.3389\/fonc.2022.866733","article-title":"Ozone Layer Depletion and Emerging Public Health Concerns-An Update on Epidemiological Perspective of the Ambivalent Effects of Ultraviolet Radiation Exposure","volume":"12","author":"Umar","year":"2022","journal-title":"Front. Oncol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lin, T.-C., and Lee, H.-C. (2020, January 17\u201319). Skin cancer dermoscopy images classification with meta data via deep learning ensemble. Proceedings of the 2020 International Computer Symposium (ICS), Tainan, Taiwan.","DOI":"10.1109\/ICS51289.2020.00055"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1111\/bjd.18247","article-title":"The incidence and survival of melanoma and nonmelanoma skin cancer in patients with vitiligo: A nationwide population-based matched cohort study in Korea","volume":"182","author":"Kim","year":"2020","journal-title":"Br. J. Dermatol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"202","DOI":"10.3322\/caac.21718","article-title":"Cancer statistics for African American\/Black People 2022","volume":"72","author":"Giaquinto","year":"2022","journal-title":"CA A Cancer J. Clin."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"112","DOI":"10.3322\/caac.21703","article-title":"American Cancer Society\u2019s report on the status of cancer disparities in the United States, 2021","volume":"72","author":"Islami","year":"2022","journal-title":"CA A Cancer J. Clin."},{"key":"ref_20","unstructured":"Iwagami, M., Caplin, B., Smeeth, L., Tomlinson, L.A., and Nitsch, D. (2018). Clinical Codelist\u2014Read Codes for Hypothyroidism, London School of Hygiene & Tropical Medicine. Data Collection."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1001\/jamadermatol.2022.0160","article-title":"Global burden of cutaneous melanoma in 2020 and projections to 2040","volume":"158","author":"Arnold","year":"2022","journal-title":"JAMA Dermatol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1111\/bjd.21274","article-title":"Does the morphology of cutaneous melanoma help explain the international differences in survival? Results from 1,578,482 adults diagnosed during 2000\u20132014 in 59 countries (CONCORD-3)","volume":"187","author":"Stiller","year":"2022","journal-title":"Br. J. Dermatol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Perez, E., and Ventura, S. (2022, January 1\u20133). Multi-view Deep Neural Networks for multiclass skin lesion diagnosis. Proceedings of the 2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS), Barcelona, Spain.","DOI":"10.1109\/COINS54846.2022.9854997"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"212","DOI":"10.3322\/caac.20121","article-title":"Cancer statistics, 2011: The impact of eliminating socioeconomic and racial disparities on premature cancer deaths","volume":"61","author":"Siegel","year":"2011","journal-title":"CA A Cancer J. Clin."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1054\/bjoc.2000.1599","article-title":"The prognostic advantage of preoperative intratumoral injection of OK-432 for gastric cancer patients","volume":"84","author":"Gochi","year":"2001","journal-title":"Br. J. Cancer"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s10147-007-0751-1","article-title":"Statistical profiles of malignant melanoma and other skin cancers in Japan: 2007 update","volume":"13","author":"Ishihara","year":"2008","journal-title":"Int. J. Clin. Oncol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1111\/j.1524-4725.1996.tb00312.x","article-title":"The epidemiology of skin cancer","volume":"22","author":"Gloster","year":"1996","journal-title":"Dermatol. Surg."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1136\/ard.49.6.363","article-title":"Mortality and survival in rheumatoid arthritis: A 25 year prospective study of 100 patients","volume":"49","author":"Reilly","year":"1990","journal-title":"Ann. Rheum. Dis."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1289\/ehp.7719109","article-title":"Effects and dose-response relationships of skin cancer and blackfoot disease with arsenic","volume":"19","author":"Tseng","year":"1977","journal-title":"Environ. Health Perspect."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1136\/jnnp.50.5.523","article-title":"Survival pattern and cause of death in patients with multiple sclerosis: Results from an epidemiological survey in north east Scotland","volume":"50","author":"Phadke","year":"1987","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"ref_31","first-page":"1148","article-title":"A Case of Rectal Cancer with Brain and Skin Metastasis with Long-Term Survival Managed by Multidisciplinary Therapy","volume":"49","author":"Tsuchiya","year":"2022","journal-title":"Gan Kagaku Ryoho"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.compmedimag.2008.11.002","article-title":"Lesion border detection in dermoscopy images","volume":"33","author":"Celebi","year":"2009","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"33","DOI":"10.3233\/jid-2016-0002","article-title":"Early skin cancer detection using computer aided diagnosis techniques","volume":"20","author":"Fernandes","year":"2016","journal-title":"J. Integr. Des. Process. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1007\/s10619-021-07360-z","article-title":"Deep learning-based computer aided diagnosis model for skin cancer detection and classification","volume":"40","author":"Adla","year":"2022","journal-title":"Distrib. Parallel Databases"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Tsai, C.L., Mukundan, A., Chung, C.S., Chen, Y.H., Wang, Y.K., Chen, T.H., Tseng, Y.S., Huang, C.W., Wu, I.C., and Wang, H.C. (2021). Hyperspectral Imaging Combined with Artificial Intelligence in the Early Detection of Esophageal Cancer. Cancers, 13.","DOI":"10.3390\/cancers13184593"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1515\/med-2020-0131","article-title":"Computer-aided diagnosis of skin cancer based on soft computing techniques","volume":"15","author":"Xu","year":"2020","journal-title":"Open Med."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Jaleel, J.A., Salim, S., and Aswin, R. (2013, January 20\u201321). Computer aided detection of skin cancer. Proceedings of the 2013 International Conference on Circuits, Power and Computing Technologies (ICCPCT), Nagercoil, India.","DOI":"10.1109\/ICCPCT.2013.6528879"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"108359","DOI":"10.1016\/j.compeleceng.2022.108359","article-title":"Optimization driven model and segmentation network for skin cancer detection","volume":"103","author":"Kumar","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"31219","DOI":"10.1007\/s11042-020-09637-4","article-title":"Efficient fusion of handcrafted and pre-trained CNNs features to classify melanoma skin cancer","volume":"79","author":"Filali","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1109\/MP.2006.1649009","article-title":"Biosensors: A tutorial review","volume":"25","author":"Mohanty","year":"2006","journal-title":"Ieee Potentials"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"108318","DOI":"10.1016\/j.compeleceng.2022.108318","article-title":"Optimal deep neural network-driven computer aided diagnosis model for skin cancer","volume":"103","author":"Malibari","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1111\/exd.14301","article-title":"In vivo diagnosis of skin cancer with a portable Raman spectroscopic device","volume":"30","author":"Bratchenko","year":"2021","journal-title":"Exp. Dermatol."},{"key":"ref_43","first-page":"1","article-title":"Biosensors: The new wave in cancer diagnosis","volume":"4","author":"Bohunicky","year":"2010","journal-title":"Nanotechnol. Sci. Appl."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1109\/JSEN.2018.2882363","article-title":"Water-based terahertz metamaterial for skin cancer detection application","volume":"19","author":"Keshavarz","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_45","first-page":"293","article-title":"An overview on biosensors","volume":"7","author":"Lalitha","year":"2017","journal-title":"Int. J. Pharm. Chem. Biol. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"147858","DOI":"10.1109\/ACCESS.2020.3014701","article-title":"Region-of-interest based transfer learning assisted framework for skin cancer detection","volume":"8","author":"Ashraf","year":"2020","journal-title":"IEEE Access"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.compmedimag.2019.01.006","article-title":"Novel lymph node segmentation and proliferation index measurement for skin melanoma biopsy images","volume":"73","author":"Alheejawi","year":"2019","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Vocaturo, E., Perna, D., and Zumpano, E. (2019, January 18\u201321). Machine learning techniques for automated melanoma detection. Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA.","DOI":"10.1109\/BIBM47256.2019.8983165"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Rey-Barroso, L., Pe\u00f1a-Guti\u00e9rrez, S., Y\u00e1\u00f1ez, C., Burgos-Fern\u00e1ndez, F.J., Vilaseca, M., and Royo, S. (2021). Optical technologies for the improvement of skin cancer diagnosis: A review. Sensors, 21.","DOI":"10.3390\/s21010252"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1109\/JBHI.2021.3052044","article-title":"A visually interpretable deep learning framework for histopathological image-based skin cancer diagnosis","volume":"25","author":"Jiang","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1272","DOI":"10.1002\/jemt.23686","article-title":"Computer vision for microscopic skin cancer diagnosis using handcrafted and non-handcrafted features","volume":"84","author":"Saba","year":"2021","journal-title":"Microsc. Res. Tech."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1319","DOI":"10.1007\/s11036-020-01550-2","article-title":"A de-ann inspired skin cancer detection approach using fuzzy c-means clustering","volume":"25","author":"Kumar","year":"2020","journal-title":"Mob. Netw. Appl."},{"key":"ref_53","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_54","first-page":"270","article-title":"SVM classifier on chip for melanoma detection","volume":"2017","author":"Afifi","year":"2017","journal-title":"Annu. Int. Conf. IEEE Eng. Med. Biol. Soc."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Thiem, D.G., R\u00f6mer, P., Blatt, S., Al-Nawas, B., and K\u00e4mmerer, P.W. (2021). New Approach to the Old Challenge of Free Flap Monitoring\u2014Hyperspectral Imaging Outperforms Clinical Assessment by Earlier Detection of Perfusion Failure. J. Pers. Med., 11.","DOI":"10.3390\/jpm11111101"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2018.2890023","article-title":"Multisource and multitemporal data fusion in remote sensing: A comprehensive review of the state of the art","volume":"7","author":"Ghamisi","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Li, Q., He, X., Wang, Y., Liu, H., Xu, D., and Guo, F. (2013). Review of spectral imaging technology in biomedical engineering: Achievements and challenges. J. Biomed. Opt., 18.","DOI":"10.1117\/1.JBO.18.10.100901"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Lu, G., and Fei, B. (2014). Medical hyperspectral imaging: A review. J. Biomed. Opt., 19.","DOI":"10.1117\/1.JBO.19.1.010901"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zakian, C.M., Pretty, I.A., and Ellwood, R. (2009). Near-infared hyperspectral imaging of teeth for dental caries detection. J. Biomed. Opt., 14.","DOI":"10.1117\/1.3275480"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1038\/sj.neo.7900082","article-title":"Non-invasive in vivo characterization of breast tumors using photon migration spectroscopy","volume":"2","author":"Tromberg","year":"2000","journal-title":"Neoplasia"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/S1076-6332(03)80529-9","article-title":"Sources of absorption and scattering contrast for near-infrared optical mammography","volume":"8","author":"Cerussi","year":"2001","journal-title":"Acad. Radiol."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Bi, D., Zhu, D., Sheykhahmad, F.R., and Qiao, M. (2021). Computer-aided skin cancer diagnosis based on a New meta-heuristic algorithm combined with support vector method. Biomed. Signal Process. Control, 68.","DOI":"10.1016\/j.bspc.2021.102631"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1016\/j.jenvman.2007.06.033","article-title":"Aerospace wetland monitoring by hyperspectral imaging sensors: A case study in the coastal zone of San Rossore Natural Park","volume":"90","author":"Barducci","year":"2009","journal-title":"J. Environ. Manag."},{"key":"ref_64","unstructured":"Sun, D.-W. (2010). Hyperspectral Imaging for Food Quality Analysis and Control, Elsevier."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Lu, B., Dao, P.D., Liu, J., He, Y., and Shang, J. (2020). Recent advances of hyperspectral imaging technology and applications in agriculture. Remote Sens., 12.","DOI":"10.3390\/rs12162659"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1016\/B978-0-444-63977-6.00021-3","article-title":"Hyperspectral imaging in medical applications","volume":"Volume 32","author":"Fei","year":"2020","journal-title":"Data Handling in Science and Technology"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Lee, C.-H., Mukundan, A., Chang, S.-C., Wang, Y.-L., Lu, S.-H., Huang, Y.-C., and Wang, H.-C. (2021). Comparative Analysis of Stress and Deformation between One-Fenced and Three-Fenced Dental Implants Using Finite Element Analysis. J. Clin. Med., 10.","DOI":"10.3390\/jcm10173986"},{"key":"ref_68","first-page":"380","article-title":"Hyperspectral imaging for astronomy and space surveillance","volume":"5159","author":"Hege","year":"2004","journal-title":"Imaging Spectrom. IX"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"5107","DOI":"10.1364\/BOE.428143","article-title":"Hyperspectral imaging and robust statistics in non-melanoma skin cancer analysis","volume":"12","author":"Courtenay","year":"2021","journal-title":"Biomed. Opt. Express"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Tsai, T.-J., Mukundan, A., Chi, Y.-S., Tsao, Y.-M., Wang, Y.-K., Chen, T.-H., Wu, I.-C., Huang, C.-W., and Wang, H.-C. (2022). Intelligent Identification of Early Esophageal Cancer by Band-Selective Hyperspectral Imaging. Cancers, 14.","DOI":"10.3390\/cancers14174292"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Fang, Y.-J., Mukundan, A., Tsao, Y.-M., Huang, C.-W., and Wang, H.-C. (2022). Identification of Early Esophageal Cancer by Semantic Segmentation. J. Pers. Med., 12.","DOI":"10.3390\/jpm12081204"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"101922","DOI":"10.1016\/j.pdpdt.2020.101922","article-title":"Hyperspectral imaging for diagnosis and detection of ex-vivo breast cancer","volume":"31","author":"Aboughaleb","year":"2020","journal-title":"Photodiagnosis Photodyn. Ther."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Liu, H., Yu, T., Hu, B., Hou, X., Zhang, Z., Liu, X., Liu, J., Wang, X., Zhong, J., and Tan, Z. (2021). Uav-borne hyperspectral imaging remote sensing system based on acousto-optic tunable filter for water quality monitoring. Remote Sens., 13.","DOI":"10.3390\/rs13204069"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"105621","DOI":"10.1016\/j.compag.2020.105621","article-title":"Hyperspectral imaging and 3D technologies for plant phenotyping: From satellite to close-range sensing","volume":"175","author":"Liu","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1016\/j.snb.2016.08.170","article-title":"Single seed near-infrared hyperspectral imaging in determining tomato (Solanum lycopersicum L.) seed quality in association with multivariate data analysis","volume":"237","author":"Shrestha","year":"2016","journal-title":"Sens. Actuators B Chem."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Wu, N., Liu, F., Meng, F., Li, M., Zhang, C., and He, Y. (2021). Rapid and accurate varieties classification of different crop seeds under sample-limited condition based on hyperspectral imaging and deep transfer learning. Front. Bioeng. Biotechnol., 9.","DOI":"10.3389\/fbioe.2021.696292"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Hsiao, Y.-P., Mukundan, A., Chen, W.-C., Wu, M.-T., Hsieh, S.-C., and Wang, H.-C. (2022). Design of a Lab-On-Chip for Cancer Cell Detection through Impedance and Photoelectrochemical Response Analysis. Biosensors, 12.","DOI":"10.3390\/bios12060405"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Stuart, M.B., McGonigle, A.J., and Willmott, J.R. (2019). Hyperspectral imaging in environmental monitoring: A review of recent developments and technological advances in compact field deployable systems. Sensors, 19.","DOI":"10.3390\/s19143071"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Chen, C.-W., Tseng, Y.-S., Mukundan, A., and Wang, H.-C. (2021). Air Pollution: Sensitive Detection of PM2.5 and PM10 Concentration Using Hyperspectral Imaging. Appl. Sci., 11.","DOI":"10.3390\/app11104543"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Huang, S.-Y., Mukundan, A., Tsao, Y.-M., Kim, Y., Lin, F.-C., and Wang, H.-C. (2022). Recent Advances in Counterfeit Art, Document, Photo, Hologram, and Currency Detection Using Hyperspectral Imaging. Sensors, 22.","DOI":"10.3390\/s22197308"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"18475","DOI":"10.1038\/s41598-022-22424-5","article-title":"Portable and low-cost hologram verification module using a snapshot-based hyperspectral imaging algorithm","volume":"12","author":"Mukundan","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Mukundan, A., Wang, H.-C., and Tsao, Y.-M. (2022, January 27\u201328). A Novel Multipurpose Snapshot Hyperspectral Imager used to Verify Security Hologram. Proceedings of the 2022 International Conference on Engineering and Emerging Technologies (ICEET), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICEET56468.2022.10007232"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Mukundan, A., Tsao, Y.-M., Cheng, W.-M., Lin, F.-C., and Wang, H.-C. (2023). Automatic Counterfeit Currency Detection Using a Novel Snapshot Hyperspectral Imaging Algorithm. Sensors, 23.","DOI":"10.3390\/s23042026"},{"key":"ref_84","first-page":"136","article-title":"Hyperspectral imaging technology for pharmaceutical analysis","volume":"4626","author":"Hamilton","year":"2002","journal-title":"Biomed. Nanotechnol. Archit. Appl."},{"key":"ref_85","first-page":"136","article-title":"A coarse-to-fine approach for medical hyperspectral image classification with sparse representation","volume":"10461","author":"Chang","year":"2017","journal-title":"AOPC 2017 Opt. Spectrosc. Imaging"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Yang, K.-Y., Fang, Y.-J., Karmakar, R., Mukundan, A., Tsao, Y.-M., Huang, C.-W., and Wang, H.-C. (2023). Assessment of Narrow Band Imaging Algorithm for Video Capsule Endoscopy Based on Decorrelated Color Space for Esophageal Cancer. Cancers, 15.","DOI":"10.20944\/preprints202308.0280.v1"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.forsciint.2014.06.023","article-title":"Detection of residues from explosive manipulation by near infrared hyperspectral imaging: A promising forensic tool","volume":"242","author":"Amigo","year":"2014","journal-title":"Forensic Sci. Int."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Favreau, P.F., Hernandez, C., Lindsey, A.S., Alvarez, D.F., Rich, T.C., Prabhat, P., and Leavesley, S.J. (2013). Thin-film tunable filters for hyperspectral fluorescence microscopy. J. Biomed. Opt., 19.","DOI":"10.1117\/1.JBO.19.1.011017"},{"key":"ref_89","unstructured":"Xu, D., Ni, G., Jiang, T., Jiang, L., and Chi, M. (2007, January 23\u201328). Integration of field work and hyperspectral data for oil and gas exploration. Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.talanta.2015.01.012","article-title":"Recent applications of hyperspectral imaging in microbiology","volume":"137","author":"Gowen","year":"2015","journal-title":"Talanta"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"4659","DOI":"10.1002\/jsfa.8996","article-title":"Discrimination of nitrogen fertilizer levels of tea plant (Camellia sinensis) based on hyperspectral imaging","volume":"98","author":"Wang","year":"2018","journal-title":"J. Sci. Food Agric."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Huang, S., Wang, L., Chen, W., Lin, D., Huang, L., Wu, S., Feng, S., and Chen, R. (2014, January 17). Non-invasive optical detection of esophagus cancer based on urine surface-enhanced Raman spectroscopy. Proceedings of the Twelfth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2014), Wuhan, China.","DOI":"10.1117\/12.2068859"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"1886","DOI":"10.1109\/LGRS.2014.2312754","article-title":"Singular spectrum analysis for effective feature extraction in hyperspectral imaging","volume":"11","author":"Zabalza","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Fabelo, H., Ortega, S., Lazcano, R., Madro\u00f1al, D., Callic\u00f3, G.M., Ju\u00e1rez, E., Salvador, R., Bulters, D., Bulstrode, H., and Szolna, A. (2018). An intraoperative visualization system using hyperspectral imaging to aid in brain tumor delineation. Sensors, 18.","DOI":"10.3390\/s18020430"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"4492","DOI":"10.1021\/acschemneuro.9b00331","article-title":"In vivo assessment of retinal biomarkers by hyperspectral imaging: Early detection of Alzheimer\u2019s disease","volume":"10","author":"More","year":"2019","journal-title":"ACS Chem. Neurosci."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"2583","DOI":"10.1109\/JSTARS.2014.2371438","article-title":"Progressive band processing of linear spectral unmixing for hyperspectral imagery","volume":"8","author":"Chang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Akbari, H., Halig, L.V., Zhang, H., Wang, D., Chen, Z.G., and Fei, B. (2012, January 14). Detection of cancer metastasis using a novel macroscopic hyperspectral method. Proceedings of the Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging, San Diego, CA, USA.","DOI":"10.1117\/12.912026"},{"key":"ref_98","first-page":"8887","article-title":"Classification of dermoscopy images for early detection of skin cancer\u2014A review","volume":"975","author":"Senan","year":"2019","journal-title":"Int. J. Comput. Appl."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Ortega, S., Fabelo, H., Iakovidis, D.K., Koulaouzidis, A., and Callico, G.M. (2019). Use of hyperspectral\/multispectral imaging in gastroenterology. Shedding some\u2013different\u2013light into the dark. J. Clin. Med., 8.","DOI":"10.3390\/jcm8010036"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/S0190-9622(94)70061-3","article-title":"The ABCD rule of dermatoscopy: High prospective value in the diagnosis of doubtful melanocytic skin lesions","volume":"30","author":"Nachbar","year":"1994","journal-title":"J. Am. Acad. Dermatol."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1016\/j.jaad.2015.01.025","article-title":"Early detection of melanoma: Reviewing the ABCDEs","volume":"72","author":"Tsao","year":"2015","journal-title":"J. Am. Acad. Dermatol."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/S0733-8635(18)30971-9","article-title":"The biopsy","volume":"2","author":"Peters","year":"1984","journal-title":"Dermatol. Clin."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1111\/j.1600-0846.2007.00219.x","article-title":"Digital dermoscopy in clinical practise: A three-centre analysis","volume":"13","author":"Wollina","year":"2007","journal-title":"Ski. Res. Technol."},{"key":"ref_105","unstructured":"Tudor, A., Feldman, J., and Diamandis, C. (2023, November 26). Why the ABCDE Rule Is Not Helpful but Dangerous in Skin Cancer Prevention. Zenodo, 27 November 2021. Available online: https:\/\/www.scanoma.com\/blog\/why-the-abcde-rule-is-not-helpful-but-dangerous-in-skin-cancer-prevention."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Fabelo, H., Meli\u00e1n, V., Mart\u00ednez, B., Beltr\u00e1n, P., Ortega, S., Marrero, M., Callic\u00f3, G.M., Sarmiento, R., Casta\u00f1o, I., and Carretero, G. (2019, January 20\u201322). Dermatologic hyperspectral imaging system for skin cancer diagnosis assistance. Proceedings of the 2019 XXXIV Conference on Design of Circuits and Integrated Systems (DCIS), Bilbao, Spain.","DOI":"10.1109\/DCIS201949030.2019.8959869"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1016\/j.procs.2015.04.209","article-title":"Computer aided melanoma skin cancer detection using image processing","volume":"48","author":"Jain","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_108","first-page":"42","article-title":"Development of a transcriptomic-based index to prognosticate cancer","volume":"2010","author":"Serao","year":"2010","journal-title":"ISBRA 2010"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1046\/j.1365-2133.2003.05023.x","article-title":"Pattern analysis, not simplified algorithms, is the most reliable method for teaching dermoscopy for melanoma diagnosis to residents in dermatology","volume":"148","author":"Carli","year":"2003","journal-title":"Br. J. Dermatol."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1049\/iet-ipr.2015.0385","article-title":"Classification of malignant melanoma and benign skin lesions: Implementation of automatic ABCD rule","volume":"10","author":"Kasmi","year":"2016","journal-title":"IET Image Process."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"83333","DOI":"10.1109\/ACCESS.2020.2991034","article-title":"Automating the ABCD rule for melanoma detection: A survey","volume":"8","author":"Ali","year":"2020","journal-title":"IEEE Access"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"367","DOI":"10.2340\/00015555-2239","article-title":"Validity of ABCD rule of dermoscopy in clinical practice","volume":"96","author":"Ahnlide","year":"2016","journal-title":"Acta Derm. Venereol."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/S0190-9622(99)70184-2","article-title":"Reevaluation of the ABCD rule for epiluminescence microscopy","volume":"40","author":"Binder","year":"1999","journal-title":"J. Am. Acad. Dermatol."},{"key":"ref_114","unstructured":"Milton, M.A.A. (2019). Automated skin lesion classification using ensemble of deep neural networks in ISIC 2018: Skin lesion analysis towards melanoma detection challenge. arXiv."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"5523","DOI":"10.1002\/mp.14471","article-title":"Noninvasive mid-IR fiber-optic evanescent wave spectroscopy (FEWS) for early detection of skin cancers","volume":"47","author":"Basov","year":"2020","journal-title":"Med. Phys."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1274","DOI":"10.1016\/j.jiph.2020.06.033","article-title":"Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges","volume":"13","author":"Saba","year":"2020","journal-title":"J. Infect. Public Health"},{"key":"ref_117","first-page":"357","article-title":"Early detection and treatment of skin cancer","volume":"62","author":"Jerant","year":"2000","journal-title":"Am. Fam. Physician"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"965630","DOI":"10.3389\/fphys.2022.965630","article-title":"Early and accurate detection of melanoma skin cancer using hybrid level set approach","volume":"13","author":"Ragab","year":"2022","journal-title":"Front. Physiol."},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Masood, A., and Ali Al-Jumaily, A. (2013). Computer aided diagnostic support system for skin cancer: A review of techniques and algorithms. Int. J. Biomed. Imaging, 2013.","DOI":"10.1155\/2013\/323268"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/S0738-081X(02)00236-5","article-title":"Dermoscopy: Alternative melanocytic algorithms\u2014The ABCD rule of dermatoscopy, menzies scoring method, and 7-point checklist","volume":"20","author":"Johr","year":"2002","journal-title":"Clin. Dermatol."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"2043","DOI":"10.1007\/s11831-021-09648-w","article-title":"A systematic review of artificial intelligence techniques in cancer prediction and diagnosis","volume":"29","author":"Kumar","year":"2021","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"2771","DOI":"10.1001\/jama.292.22.2771","article-title":"Early diagnosis of cutaneous melanoma: Revisiting the ABCD criteria","volume":"292","author":"Abbasi","year":"2004","journal-title":"JAMA"},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Burlina, P., Billings, S., Joshi, N., and Albayda, J. (2017). Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0184059"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"2388","DOI":"10.1016\/S0140-6736(18)31645-3","article-title":"Deep learning algorithms for detection of critical findings in head CT scans: A retrospective study","volume":"392","author":"Chilamkurthy","year":"2018","journal-title":"Lancet"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"3286","DOI":"10.21037\/qims-20-1356","article-title":"Comparative evaluation of conventional and deep learning methods for semi-automated segmentation of pulmonary nodules on CT","volume":"11","author":"Bianconi","year":"2021","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Javed, R., Rahim, M.S.M., Saba, T., and Rehman, A. (2020). A comparative study of features selection for skin lesion detection from dermoscopic images. Netw. Model. Anal. Health Inform. Bioinform., 9.","DOI":"10.1007\/s13721-019-0209-1"},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.1109\/JBHI.2019.2891049","article-title":"Deep learning and handcrafted method fusion: Higher diagnostic accuracy for melanoma dermoscopy images","volume":"23","author":"Hagerty","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","article-title":"QUADAS-2: A revised tool for the quality assessment of diagnostic accuracy studies","volume":"155","author":"Whiting","year":"2011","journal-title":"Ann. Intern. Med."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1179\/106698107790819945","article-title":"Creation and critique of studies of diagnostic accuracy: Use of the STARD and QUADAS methodological quality assessment tools","volume":"15","author":"Cook","year":"2007","journal-title":"J. Man. Manip. Ther."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"1592","DOI":"10.7326\/M21-2234","article-title":"QUADAS-C: A tool for assessing risk of bias in comparative diagnostic accuracy studies","volume":"174","author":"Yang","year":"2021","journal-title":"Ann. Intern. Med."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"1663","DOI":"10.1038\/s41591-021-01517-0","article-title":"A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI","volume":"27","author":"Sounderajah","year":"2021","journal-title":"Nat. Med."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"323","DOI":"10.7326\/0003-4819-156-4-201202210-00018","article-title":"The revised QUADAS-2 tool","volume":"156","author":"Schueler","year":"2012","journal-title":"Ann. Intern. Med."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.7326\/M22-0276","article-title":"QUAPAS: An Adaptation of the QUADAS-2 Tool to Assess Prognostic Accuracy Studies","volume":"175","author":"Lee","year":"2022","journal-title":"Ann. Intern. Med."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1007\/s00127-008-0440-z","article-title":"Assessing the quality of diagnostic studies using psychometric instruments: Applying QUADAS","volume":"44","author":"Mann","year":"2009","journal-title":"Soc. Psychiatry Psychiatr. Epidemiol."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"1316","DOI":"10.1016\/j.clinbiochem.2008.06.018","article-title":"QUADOMICS: An adaptation of the Quality Assessment of Diagnostic Accuracy Assessment (QUADAS) for the evaluation of the methodological quality of studies on the diagnostic accuracy of \u2018-omics\u2019-based technologies","volume":"41","author":"Lumbreras","year":"2008","journal-title":"Clin. Biochem."},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"Leon, R., Martinez-Vega, B., Fabelo, H., Ortega, S., Melian, V., Casta\u00f1o, I., Carretero, G., Almeida, P., Garcia, A., and Quevedo, E. (2020). Non-invasive skin cancer diagnosis using hyperspectral imaging for in-situ clinical support. J. Clin. Med., 9.","DOI":"10.3390\/jcm9061662"},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Lindholm, V., Raita-Hakola, A.-M., Annala, L., Salmivuori, M., Jeskanen, L., Saari, H., Koskenmies, S., Pitk\u00e4nen, S., P\u00f6l\u00f6nen, I., and Isoherranen, K. (2022). Differentiating Malignant from Benign Pigmented or Non-Pigmented Skin Tumours\u2014A Pilot Study on 3D Hyperspectral Imaging of Complex Skin Surfaces and Convolutional Neural Networks. J. Clin. Med., 11.","DOI":"10.3390\/jcm11071914"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1111\/srt.13023","article-title":"Clinical performance of a novel hyperspectral imaging device for cutaneous melanoma and pigmented skin lesions in Caucasian skin","volume":"27","author":"Christensen","year":"2021","journal-title":"Ski. Res. Technol."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1002\/lsm.23055","article-title":"Hyperspectral imaging in automated digital dermoscopy screening for melanoma","volume":"51","author":"Hosking","year":"2019","journal-title":"Lasers Surg. Med."},{"key":"ref_140","unstructured":"Pozhar, V.E., Machikhin, A.S., Myakinin, O.O., and Bratchenko, I.A. (2020). Multimodal Optical Diagnostics of Cancer, Springer."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"6283","DOI":"10.1364\/BOE.9.006283","article-title":"On the spectral signature of melanoma: A non-parametric classification framework for cancer detection in hyperspectral imaging of melanocytic lesions","volume":"9","author":"Pardo","year":"2018","journal-title":"Biomed. Opt. Express"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"012026","DOI":"10.1088\/1742-6596\/2127\/1\/012026","article-title":"Neural network classifier for hyperspectral images of skin pathologies","volume":"2127","author":"Vinokurov","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"adv00405","DOI":"10.2340\/00015555-3755","article-title":"Hyperspectral Imaging Reveals Spectral Differences and Can Distinguish Malignant Melanoma from Pigmented Basal Cell Carcinomas: A Pilot Study","volume":"101","author":"Salmivuori","year":"2021","journal-title":"Acta Derm. Venereol."},{"key":"ref_144","doi-asserted-by":"crossref","unstructured":"Nagaoka, T., Nakamura, A., Kiyohara, Y., and Sota, T. (2012\u20131, January 28). Melanoma screening system using hyperspectral imager attached to imaging fiberscope. Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, USA.","DOI":"10.1109\/EMBC.2012.6346777"},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Zherdeva, L.A., Bratchenko, I.A., Myakinin, O.O., Moryatov, A.A., Kozlov, S.V., and Zakharov, V.P. (2016, January 31). In vivo hyperspectral imaging and differentiation of skin cancer. Proceedings of the Optics in Health Care and Biomedical Optics VII, Beijing, China.","DOI":"10.1117\/12.2246433"},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"Huang, H.-Y., Hsiao, Y.-P., Mukundan, A., Tsao, Y.-M., Chang, W.-Y., and Wang, H.-C. (2023). Classification of Skin Cancer Using Novel Hyperspectral Imaging Engineering via YOLOv5. J. Clin. Med., 12.","DOI":"10.3390\/jcm12031134"},{"key":"ref_147","first-page":"67","article-title":"Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS implementations","volume":"19","author":"Zhu","year":"2010","journal-title":"NESUG Proc. Health Care Life Sci. Baltim. Md."},{"key":"ref_148","first-page":"14","article-title":"Incidence of nonmelanoma skin cancer in renal transplant recipients: A systematic review and meta-analysis","volume":"23","author":"Matinfar","year":"2018","journal-title":"J. Res. Med. Sci. Off. J. Isfahan Univ. Med. Sci."},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.critrevonc.2017.12.003","article-title":"Anti-hypertensive drugs and skin cancer risk: A review of the literature and meta-analysis","volume":"122","author":"Gandini","year":"2018","journal-title":"Crit. Rev. Oncol. Hematol."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s40257-020-00562-8","article-title":"Laser treatment for non-melanoma skin cancer: A systematic review and meta-analysis","volume":"22","author":"Sharon","year":"2021","journal-title":"Am. J. Clin. Dermatol."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1007\/s10552-020-01319-8","article-title":"The association between smoking and risk of skin cancer: A meta-analysis of cohort studies","volume":"31","author":"Arafa","year":"2020","journal-title":"Cancer Causes Control"},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"3490","DOI":"10.1111\/ajt.13863","article-title":"Azathioprine and risk of skin cancer in organ transplant recipients: Systematic review and meta-analysis","volume":"16","author":"Jiyad","year":"2016","journal-title":"Am. J. Transplant."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1016\/S0895-4356(03)00177-X","article-title":"The diagnostic odds ratio: A single indicator of test performance","volume":"56","author":"Glas","year":"2003","journal-title":"J. Clin. Epidemiol."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1037\/a0031544","article-title":"Revisiting the serotonin\u2013aggression relation in humans: A meta-analysis","volume":"139","author":"Duke","year":"2013","journal-title":"Psychol. Bull."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1038\/s41559-019-0958-3","article-title":"A meta-analysis of 1119 manipulative experiments on terrestrial carbon-cycling responses to global change","volume":"3","author":"Song","year":"2019","journal-title":"Nat. Ecol. Evol."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1037\/edu0000490","article-title":"The effect of epistemic cognition interventions on academic achievement: A meta-analysis","volume":"113","author":"Cartiff","year":"2021","journal-title":"J. Educ. Psychol."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1037\/edu0000263","article-title":"A meta-analytic review of the relationship between epistemic cognition and academic achievement","volume":"110","author":"Greene","year":"2018","journal-title":"J. Educ. Psychol."}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/15\/23\/5634\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:33:25Z","timestamp":1760132005000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/15\/23\/5634"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,29]]},"references-count":157,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["cancers15235634"],"URL":"https:\/\/doi.org\/10.3390\/cancers15235634","relation":{},"ISSN":["2072-6694"],"issn-type":[{"value":"2072-6694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,29]]}}}