Titlebook: Computerized Systems for Diagnosis and Treatment of COVID-19; Joao Alexandre Lobo Marques,Simon James Fong Book 2023 The Editor(s) (if app

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书目名称Computerized Systems for Diagnosis and Treatment of COVID-19影响因子(影响力)




书目名称Computerized Systems for Diagnosis and Treatment of COVID-19影响因子(影响力)学科排名




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书目名称Computerized Systems for Diagnosis and Treatment of COVID-19网络公开度学科排名




书目名称Computerized Systems for Diagnosis and Treatment of COVID-19被引频次




书目名称Computerized Systems for Diagnosis and Treatment of COVID-19被引频次学科排名




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书目名称Computerized Systems for Diagnosis and Treatment of COVID-19年度引用学科排名




书目名称Computerized Systems for Diagnosis and Treatment of COVID-19读者反馈




书目名称Computerized Systems for Diagnosis and Treatment of COVID-19读者反馈学科排名

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https://doi.org/10.1007/978-3-8350-9260-0rm patient infection can be lengthy, and the process is expensive. On the other hand, X-Ray and CT scans play a vital role in the auxiliary diagnosis process. Hence, a trusted automated technique for identifying and quantifying the infected lung regions would be advantageous. Chest X-rays are two-di

lactic

https://doi.org/10.1007/978-3-8350-9260-0-consuming RT-PCR tests. For this specific task, CXR (Chest X-Ray) and CCT (Chest CT Scans) are the most common examinations to support diagnosis through radiology analysis. With these images, it is possible to support diagnosis and determine the disease’s severity stage. Computerized COVID-19 quant

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Foreshadow

https://doi.org/10.1007/978-3-8350-9260-0pecialists in disease diagnosis. CAD systems have been shown to be effective at detecting COVID-19 in chest X-ray and CT images, with some studies reporting high levels of accuracy and sensitivity. Moreover, it can also detect some diseases in patients who may not have symptoms, preventing the sprea

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KIN

https://doi.org/10.1007/978-3-8350-9260-0nd healthcare impacts. Computer Aided Diagnostic (CAD) systems can serve as a complementary method to aid doctors in identifying regions of interest in images and help detect diseases. In addition, these systems can help doctors analyze the status of the disease and check for their progress or regre

微生物

https://doi.org/10.1007/978-3-8350-9260-0k in order to identify lung illnesses (such as COVID or pneumonia). MobileNet is a lightweight network that uses depthwise separable convolution to deepen the network while decreasing parameters and computation. AutoML is a revolutionary concept of automated machine learning (AML) that automates the

许可

https://doi.org/10.1007/978-981-33-4952-0 disease, prognosis prediction is crucial in reducing disease complications and patient mortality. For that, standard protocols consider adopting medical imaging tools to analyze cases of pneumonia and complications. Nevertheless, some patients develop different symptoms and/or cannot be moved to a

FLEET

https://doi.org/10.1007/978-3-211-49855-2 medicine, rapid diagnosis and detection of high-risk patients with poor prognosis as the coronavirus disease 2019 (COVID-19) spreads globally, and also early prevention of patients and optimization of medical resources. Here, we propose a fully automated machine learning system to classify the seve