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Automated Diagnostic Radiographs of COVID-19 Based on Deep Learning Method
2022 IET International Conference on Engineering Technologies and Applications, IET-ICETA 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2191944
ABSTRACT
The COVID-19 outbreak has had a serious impact on Taiwan's health care system. Deep Learning is an effective technology to help doctors make the most appropriate medical decisions for every patient in this crisis. In this study, we select four state-of-the-art Deep Learning-Xception, MobileNetv2, DenseNet169, and DenseNet201. Additionally, Transfer Learning is used for pre-training them before four models individually classify normal and positive COVID-19 chest X-ray images. Lastly, the best results reached 98.58% accuracy, 98.58% precision, and 98.42% recall. © 2022 IEEE.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 2022 IET International Conference on Engineering Technologies and Applications, IET-ICETA 2022 Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 2022 IET International Conference on Engineering Technologies and Applications, IET-ICETA 2022 Year: 2022 Document Type: Article