Alexandre Krebs, V. Camilo, E. Touati, Y. Benezeth, V. Michel, G. Jouvion, Fan Yang, D. Lamarque, F. Marzani
{"title":"漫反射分析法检测幽门螺杆菌性胃炎","authors":"Alexandre Krebs, V. Camilo, E. Touati, Y. Benezeth, V. Michel, G. Jouvion, Fan Yang, D. Lamarque, F. Marzani","doi":"10.1109/BIBE.2018.00063","DOIUrl":null,"url":null,"abstract":"Spectral acquisitions contain rich information and thus, are promising modalities for early detection of gastric diseases. In this study, we analyze the diffuse reflectance of the gastric inflammatory lesions induced by the bacterium H. pylori in the mouse stomach. A pipeline has been designed to characterize and classify spectra acquired on mice. The pipeline is based on a band clustering algorithm followed by the computation of meaningful division and subtraction features and by classification with a linear SVM classifier. Currently, the pipeline is able to recognize inflamed stomach's spectra with an accuracy of 98%. These results are promising and the same pipeline could be adapted for the study of gastric pathologies in humans.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"[Regular Paper] Detection of H. pylori Induced Gastric Inflammation by Diffuse Reflectance Analysis\",\"authors\":\"Alexandre Krebs, V. Camilo, E. Touati, Y. Benezeth, V. Michel, G. Jouvion, Fan Yang, D. Lamarque, F. Marzani\",\"doi\":\"10.1109/BIBE.2018.00063\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Spectral acquisitions contain rich information and thus, are promising modalities for early detection of gastric diseases. In this study, we analyze the diffuse reflectance of the gastric inflammatory lesions induced by the bacterium H. pylori in the mouse stomach. A pipeline has been designed to characterize and classify spectra acquired on mice. The pipeline is based on a band clustering algorithm followed by the computation of meaningful division and subtraction features and by classification with a linear SVM classifier. Currently, the pipeline is able to recognize inflamed stomach's spectra with an accuracy of 98%. These results are promising and the same pipeline could be adapted for the study of gastric pathologies in humans.\",\"PeriodicalId\":127507,\"journal\":{\"name\":\"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)\",\"volume\":\"66 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIBE.2018.00063\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIBE.2018.00063","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
[Regular Paper] Detection of H. pylori Induced Gastric Inflammation by Diffuse Reflectance Analysis
Spectral acquisitions contain rich information and thus, are promising modalities for early detection of gastric diseases. In this study, we analyze the diffuse reflectance of the gastric inflammatory lesions induced by the bacterium H. pylori in the mouse stomach. A pipeline has been designed to characterize and classify spectra acquired on mice. The pipeline is based on a band clustering algorithm followed by the computation of meaningful division and subtraction features and by classification with a linear SVM classifier. Currently, the pipeline is able to recognize inflamed stomach's spectra with an accuracy of 98%. These results are promising and the same pipeline could be adapted for the study of gastric pathologies in humans.