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Fluorescence spectral images of 76 patients who were pathologically diagnosed as non\u2010atrophic gastritis, premalignant lesions and gastric cancer were collected. Fluorescence spectra at 100\u2010pixel points were randomly extracted after binarization. Diagnostic models of non\u2010atrophic gastritis, premalignant lesions and gastric cancer were constructed through partial\u2010least\u2010square discriminant analysis (PLS\u2010DA) and support vector machine (SVM) algorithms. The prediction effects of PLS\u2010DA and SVM models were compared. Results showed that the average spectra of normal, precancerous and gastric cancer tissues significantly differed at 496, 546, 640 and 670\u2009nm, and regular changes in fluorescence intensity at 546\u2009nm were in the following order: normal\u2009&gt;\u2009precancerous lesions\u2009&gt;\u2009gastric cancer. Additionally, the effect of the diagnostic model established by SVM is significantly better than PLS\u2010DA which accuracy, specificity and sensitivity are above 94%. Experimental results revealed that the fast diagnostic model of early gastric cancer by combining fluorescence hyperspectral imaging technology and improved SVM was effective and feasible, thereby providing an accurate and rapid method for diagnosing early\u2010stage gastric cancer.<\/jats:p><jats:p><jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"graphic\/jbio201800324-gra-0001.png\" xlink:title=\"image\"\/><\/jats:p>","DOI":"10.1002\/jbio.201800324","type":"journal-article","created":{"date-parts":[[2018,12,26]],"date-time":"2018-12-26T02:25:59Z","timestamp":1545791159000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Diagnosis of early gastric cancer based on fluorescence hyperspectral imaging technology combined with partial\u2010least\u2010square discriminant analysis and support vector 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