Journal of X-ray science and technology
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Pneumonia caused by COVID-19 shares overlapping imaging manifestations with other types of pneumonia. How to objectively and quantitatively differentiate pneumonia patients with and without COVID-19 virus remains clinical challenge. ⋯ The proposed quantitative scoring criteria showed high sensitivity and moderate specificity in detecting COVID-19 using CT images, which indicates that these criteria may be beneficial for screening in real-world practice and helpful for long-term disease control.
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To assess prognosis or dynamic change from initial diagnosis until recovery of the patients with moderate coronavirus disease (COVID-19) pneumonia using chest CT images. ⋯ The moderate COVID-19 pneumonia CT score increased rapidly in a short period of time initially, followed by a slow decline over a relatively long time. The peak of the course occurred in stage 2. Complete recovery of patients with moderate COVID-19 pneumonia with high mean CT score at the time of discharge requires longer time.
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Recently, COVID-19 has spread in more than 100 countries and regions around the world, raising grave global concerns. COVID-19 transmits mainly through respiratory droplets and close contacts, causing cluster infections. The symptoms are dominantly fever, fatigue, and dry cough, and can be complicated with tiredness, sore throat, and headache. ⋯ The main manifestation in the absorption stage is interstitial change of both lungs, such as fibrous cords and reticular opacities. Differentiation between COVID-19 pneumonia and other viral pneumonias are also analyzed. Thus, CT examination can help reduce false negatives of nucleic acid tests.
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Since CAD (Computer Aided Diagnosis) system can make it easier and more efficient to interpret CT (Computer Tomography) images, it has gained much attention and developed rapidly in recent years. This article reviews recent CAD techniques for pulmonary nodule detection and diagnosis in CT Images. ⋯ We summarized the current tendency and limitations as well as future challenges in this field. The development of CAD needs to meet the rigid clinical requirements, such as high accuracy, strong robustness, high efficiency, fine-grained analysis and classification, and to provide practical clinical functions. This review provides helpful information for both engineering researchers and radiologists to learn the latest development of CAD systems.
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Comparative Study
Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches.
The novel coronavirus disease 2019 (COVID-19) constitutes a public health emergency globally. The number of infected people and deaths are proliferating every day, which is putting tremendous pressure on our social and healthcare system. Rapid detection of COVID-19 cases is a significant step to fight against this virus as well as release pressure off the healthcare system. ⋯ This study demonstrates the effectiveness of deep transfer learning techniques for the identification of COVID-19 cases using CXR images.