Daniella Lúmara Peres, Sajid Farooq, Rocío Raffaeli, M. Croce, Adela E. Croce, D. Zezell
{"title":"利用FTIR和机器学习鉴别基底细胞癌皮肤癌","authors":"Daniella Lúmara Peres, Sajid Farooq, Rocío Raffaeli, M. Croce, Adela E. Croce, D. Zezell","doi":"10.1109/OMN/SBFotonIOPC58971.2023.10230945","DOIUrl":null,"url":null,"abstract":"Here we applied ATR-FTIR spectroscopy combined with computational modeling based on 3D-discriminant analysis (3D-PCA-QDA). Our results present an exceptional performance of 3D-discriminant algorithms to diagnose BCC skin cancer, indicating the accuracy up to 99%.","PeriodicalId":31141,"journal":{"name":"Netcom","volume":"33 1","pages":"1-2"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identification of Basal Cell Carcinoma Skin Cancer using FTIR and Machine Learning\",\"authors\":\"Daniella Lúmara Peres, Sajid Farooq, Rocío Raffaeli, M. Croce, Adela E. Croce, D. Zezell\",\"doi\":\"10.1109/OMN/SBFotonIOPC58971.2023.10230945\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Here we applied ATR-FTIR spectroscopy combined with computational modeling based on 3D-discriminant analysis (3D-PCA-QDA). Our results present an exceptional performance of 3D-discriminant algorithms to diagnose BCC skin cancer, indicating the accuracy up to 99%.\",\"PeriodicalId\":31141,\"journal\":{\"name\":\"Netcom\",\"volume\":\"33 1\",\"pages\":\"1-2\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Netcom\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/OMN/SBFotonIOPC58971.2023.10230945\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Netcom","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/OMN/SBFotonIOPC58971.2023.10230945","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Identification of Basal Cell Carcinoma Skin Cancer using FTIR and Machine Learning
Here we applied ATR-FTIR spectroscopy combined with computational modeling based on 3D-discriminant analysis (3D-PCA-QDA). Our results present an exceptional performance of 3D-discriminant algorithms to diagnose BCC skin cancer, indicating the accuracy up to 99%.
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