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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書目名稱Computerized Systems for Diagnosis and Treatment of COVID-19影響因子(影響力)




書目名稱Computerized Systems for Diagnosis and Treatment of COVID-19影響因子(影響力)學科排名




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書目名稱Computerized Systems for Diagnosis and Treatment of COVID-19網(wǎng)絡(luò)公開度學科排名




書目名稱Computerized Systems for Diagnosis and Treatment of COVID-19被引頻次




書目名稱Computerized Systems for Diagnosis and Treatment of COVID-19被引頻次學科排名




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書目名稱Computerized Systems for Diagnosis and Treatment of COVID-19年度引用學科排名




書目名稱Computerized Systems for Diagnosis and Treatment of COVID-19讀者反饋




書目名稱Computerized Systems for Diagnosis and Treatment of COVID-19讀者反饋學科排名

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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

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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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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

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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