{"title":"利用高效netv2像素色放大技术检测糖尿病视网膜病变","authors":"Yi-Hsuan Kao, Chun-Ling Lin","doi":"10.1109/ICASI57738.2023.10179565","DOIUrl":null,"url":null,"abstract":"This study adopts a pixel color amplification to increase the characteristics of the fundus image to solve inconsistent images quality problem and adopts EfficientNetV2 architecture of deep convolutional neural network (CNN) to detect Diabetic Retinopathy (DR). The results show that the proposed method can achieve 0.9120/87.16% of quadratic weight kappa and accuracy score and proves the efficacy of the proposed approach in DR classification. Thus, our work can help DR patients to reduce the probability of having lifelong blindness.","PeriodicalId":281254,"journal":{"name":"2023 9th International Conference on Applied System Innovation (ICASI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Detection of Diabetic Retinopathy via Pixel Color Amplification Using EfficientNetV2\",\"authors\":\"Yi-Hsuan Kao, Chun-Ling Lin\",\"doi\":\"10.1109/ICASI57738.2023.10179565\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study adopts a pixel color amplification to increase the characteristics of the fundus image to solve inconsistent images quality problem and adopts EfficientNetV2 architecture of deep convolutional neural network (CNN) to detect Diabetic Retinopathy (DR). The results show that the proposed method can achieve 0.9120/87.16% of quadratic weight kappa and accuracy score and proves the efficacy of the proposed approach in DR classification. Thus, our work can help DR patients to reduce the probability of having lifelong blindness.\",\"PeriodicalId\":281254,\"journal\":{\"name\":\"2023 9th International Conference on Applied System Innovation (ICASI)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 9th International Conference on Applied System Innovation (ICASI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICASI57738.2023.10179565\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 9th International Conference on Applied System Innovation (ICASI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASI57738.2023.10179565","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Detection of Diabetic Retinopathy via Pixel Color Amplification Using EfficientNetV2
This study adopts a pixel color amplification to increase the characteristics of the fundus image to solve inconsistent images quality problem and adopts EfficientNetV2 architecture of deep convolutional neural network (CNN) to detect Diabetic Retinopathy (DR). The results show that the proposed method can achieve 0.9120/87.16% of quadratic weight kappa and accuracy score and proves the efficacy of the proposed approach in DR classification. Thus, our work can help DR patients to reduce the probability of having lifelong blindness.
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