Saddam Bekhet, M. Hassaballah, Mourad A. Kenk, Mohamed Abdel Hameed
{"title":"基于人工智能的胸部x线诊断新冠肺炎技术","authors":"Saddam Bekhet, M. Hassaballah, Mourad A. Kenk, Mohamed Abdel Hameed","doi":"10.1109/NILES50944.2020.9257930","DOIUrl":null,"url":null,"abstract":"The COVID-19 pandemic had a catastrophic impact on world health and economic. This is attributed to the unavoidable delay in the diagnosis process, due to limitation of COVID-19 test kits. Thus, it is urgently required to establish more cheap and affordable diagnostic approaches. Chest X-ray is an important initial step towards a successful COVID-19 diagnose, where it is easily to detect any chest abnormalities (e.g., lung inflammation). Furthermore, majority of hospitals have X-ray devices that can be used in early COVID-19 diagnosis. However, the shortage of radiologists is a key factor that limits early COVID-19 diagnosis and negatively affects the treatment process. This paper presents an artificial intelligence based technique for early COVID-19 diagnosis from chest X-ray images using medical knowledge and deep Convolutional Neural Networks (CNNs). To this end, a deep learning model is built carefully and fine-tuned to achieve the maximum performance in COVID-19 detection. Experimental results on recent benchmark datasets demonstrate the superior performance of the proposed technique in identifying COVID-19 with 96% accuracy.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":"{\"title\":\"An Artificial Intelligence Based Technique for COVID-19 Diagnosis from Chest X-Ray\",\"authors\":\"Saddam Bekhet, M. Hassaballah, Mourad A. Kenk, Mohamed Abdel Hameed\",\"doi\":\"10.1109/NILES50944.2020.9257930\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The COVID-19 pandemic had a catastrophic impact on world health and economic. This is attributed to the unavoidable delay in the diagnosis process, due to limitation of COVID-19 test kits. Thus, it is urgently required to establish more cheap and affordable diagnostic approaches. Chest X-ray is an important initial step towards a successful COVID-19 diagnose, where it is easily to detect any chest abnormalities (e.g., lung inflammation). Furthermore, majority of hospitals have X-ray devices that can be used in early COVID-19 diagnosis. However, the shortage of radiologists is a key factor that limits early COVID-19 diagnosis and negatively affects the treatment process. This paper presents an artificial intelligence based technique for early COVID-19 diagnosis from chest X-ray images using medical knowledge and deep Convolutional Neural Networks (CNNs). To this end, a deep learning model is built carefully and fine-tuned to achieve the maximum performance in COVID-19 detection. Experimental results on recent benchmark datasets demonstrate the superior performance of the proposed technique in identifying COVID-19 with 96% accuracy.\",\"PeriodicalId\":253090,\"journal\":{\"name\":\"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"23\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NILES50944.2020.9257930\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NILES50944.2020.9257930","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Artificial Intelligence Based Technique for COVID-19 Diagnosis from Chest X-Ray
The COVID-19 pandemic had a catastrophic impact on world health and economic. This is attributed to the unavoidable delay in the diagnosis process, due to limitation of COVID-19 test kits. Thus, it is urgently required to establish more cheap and affordable diagnostic approaches. Chest X-ray is an important initial step towards a successful COVID-19 diagnose, where it is easily to detect any chest abnormalities (e.g., lung inflammation). Furthermore, majority of hospitals have X-ray devices that can be used in early COVID-19 diagnosis. However, the shortage of radiologists is a key factor that limits early COVID-19 diagnosis and negatively affects the treatment process. This paper presents an artificial intelligence based technique for early COVID-19 diagnosis from chest X-ray images using medical knowledge and deep Convolutional Neural Networks (CNNs). To this end, a deep learning model is built carefully and fine-tuned to achieve the maximum performance in COVID-19 detection. Experimental results on recent benchmark datasets demonstrate the superior performance of the proposed technique in identifying COVID-19 with 96% accuracy.