Journal of X-ray science and technology
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Comparative Study
Detection of coronavirus disease from X-ray images using deep learning and transfer learning algorithms.
This study aims to employ the advantages of computer vision and medical image analysis to develop an automated model that has the clinical potential for early detection of novel coronavirus (COVID-19) infected disease. ⋯ This study demonstrated that a deep transfer learning is feasible to detect COVID-19 disease automatically from chest X-ray by training the learning model with chest X-ray images mixed with COVID-19 patients, other pneumonia affected patients and people with healthy lungs, which may help doctors more effectively make their clinical decisions. The study also gives an insight to how transfer learning was used to automatically detect the COVID-19 disease. In future studies, as the amount of available dataset increases, different convolution neutral network models could be designed to achieve the goal more efficiently.
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To evaluate the clinical and computed tomographic (CT) features in the patients with COVID-19 pneumonia confirmed by the real-time reverse transcriptase polymerase chain reaction (rRT-PCR) amplification of the viral DNA from a sputum sample. ⋯ There were some typical CT features for diagnosis of COVID-19 pneumonia. The radiologists should know these CT findings and clinical information, which could help for accurate analysis in the patients with 2019 novel coronavirus infection.
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Although computed tomography (CT) is a powerful diagnostic imaging modality for diagnosing vascular diseases, it is some what risky to human health due to the high radiation dosage. Thus, CT vendors have developed low dose computed tomography (LDCT) aiming to solve this problem. Nowadays, LDCT has gradually become a main stream of CT examination. ⋯ Low dose CTA of rabbits with 70 or 80 kVp is feasible in a 256-slice or a 64-slice CT scanner. The radiation dose from the 256-slice CTA was much lower than that from the 64-slice CTA with comparable SNR and CNR. The technique can be further applied in longitudinal monitoring of an animal stroke model in the future.
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Deep learning has made spectacular achievements in analysing natural images, but it faces challenges for medical applications partly due to inadequate images. ⋯ Model modification, model integration, and transfer learning can play important roles to identify and generate optimal deep CNN models in classifying pulmonary nodules based on CT images efficiently. Transfer learning is preferred when applying deep learning to medical imaging applications.
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To perform quantitative measurement based on the standardized uptake value (SUV) of Tc-99m methylene diphosphonate (MDP) in the normal pelvis using a single-photon emission tomography (SPECT)/computed tomography (CT) scanner. ⋯ Determination of the SUV value of the normal pelvis with 99m Tc-MDP SPECT/CT is feasible and highly reproducible. SUVs of the normal pelvis showed a relatively large variability. As a quantitative imaging biomarker, SUVs might require standardization with adequate reference data for the participant to minimize variability.