{"title":"基于CXR图像的新冠肺炎早期诊断Darknet-19模型优化","authors":"Khouloud Samrouth, Bouchra Abdelaziz","doi":"10.1109/IC2SPM56638.2022.9988909","DOIUrl":null,"url":null,"abstract":"Even after the pandemic, Covid-19 is still threatening lives and causing devastating losses to businesses. Thus, early Covid-19 diagnosis prevents the further spread of this epidemic and helps to quickly treat affected patients of coronavirus. Unlike Polymerase Chain Reaction (PCR) test, screening techniques based on Chest X-Ray (CXR) scan detect Covid-19 early even before the beginning of Covid-19 symptoms, also they are more effective and have higher detection rates. However, the CXR images suffer of some low visual quality which makes the CXR-based screening method time consuming due to the small number of radiologists. Therefore, in this paper, we propose an optimization technique for a recently developed intelligent classification system (Darknet-19) that assists radiologists in diagnosing coronavirus for patients using CXR images. In particular, our proposed optimization scheme consists first in a close-up dataset cleaning followed by advanced image enhancement as a preprocessing phase to the Darknet-19 classification model. Our experiments show that our proposed preprocessing optimization scheme improved the performance of the Darknet-19 model to reach an accuracy of 99.2%.","PeriodicalId":179072,"journal":{"name":"2022 International Conference on Smart Systems and Power Management (IC2SPM)","volume":"59 6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimization of Darknet-19 model for the early diagnosis of Covid-19 based on CXR images\",\"authors\":\"Khouloud Samrouth, Bouchra Abdelaziz\",\"doi\":\"10.1109/IC2SPM56638.2022.9988909\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Even after the pandemic, Covid-19 is still threatening lives and causing devastating losses to businesses. Thus, early Covid-19 diagnosis prevents the further spread of this epidemic and helps to quickly treat affected patients of coronavirus. Unlike Polymerase Chain Reaction (PCR) test, screening techniques based on Chest X-Ray (CXR) scan detect Covid-19 early even before the beginning of Covid-19 symptoms, also they are more effective and have higher detection rates. However, the CXR images suffer of some low visual quality which makes the CXR-based screening method time consuming due to the small number of radiologists. Therefore, in this paper, we propose an optimization technique for a recently developed intelligent classification system (Darknet-19) that assists radiologists in diagnosing coronavirus for patients using CXR images. In particular, our proposed optimization scheme consists first in a close-up dataset cleaning followed by advanced image enhancement as a preprocessing phase to the Darknet-19 classification model. Our experiments show that our proposed preprocessing optimization scheme improved the performance of the Darknet-19 model to reach an accuracy of 99.2%.\",\"PeriodicalId\":179072,\"journal\":{\"name\":\"2022 International Conference on Smart Systems and Power Management (IC2SPM)\",\"volume\":\"59 6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Smart Systems and Power Management (IC2SPM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IC2SPM56638.2022.9988909\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Smart Systems and Power Management (IC2SPM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IC2SPM56638.2022.9988909","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimization of Darknet-19 model for the early diagnosis of Covid-19 based on CXR images
Even after the pandemic, Covid-19 is still threatening lives and causing devastating losses to businesses. Thus, early Covid-19 diagnosis prevents the further spread of this epidemic and helps to quickly treat affected patients of coronavirus. Unlike Polymerase Chain Reaction (PCR) test, screening techniques based on Chest X-Ray (CXR) scan detect Covid-19 early even before the beginning of Covid-19 symptoms, also they are more effective and have higher detection rates. However, the CXR images suffer of some low visual quality which makes the CXR-based screening method time consuming due to the small number of radiologists. Therefore, in this paper, we propose an optimization technique for a recently developed intelligent classification system (Darknet-19) that assists radiologists in diagnosing coronavirus for patients using CXR images. In particular, our proposed optimization scheme consists first in a close-up dataset cleaning followed by advanced image enhancement as a preprocessing phase to the Darknet-19 classification model. Our experiments show that our proposed preprocessing optimization scheme improved the performance of the Darknet-19 model to reach an accuracy of 99.2%.