Radiologische „report turnaround times“ und Untersuchungszeitpunkte bei Patienten auf Intensivstationen

Albrecht, L.; Busse, Reinhard; Tepe, H.; Poschmann, R.; Teichgräber, Ulf; Hamm, Bernd; Bucourt, Maximilian de

doi:10.1007/s00117-013-2537-y

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…All things considered, radiography assessment is quicker and has more prominent accessibility than RT-PCR testing, given the availability of chest radiology imaging systems in the healthcare sector. In addition, the turnaround time for X-ray examination is approximately 5.08 h [22]. Chest X-ray imaging is frequently used as a standard testing technique for respiratory complaints [23] and is easily accessible, making it a suitable COVID-19 detection method.…”

Section: Introductionmentioning

confidence: 99%

On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays

Okolo

Katsigiannis

Althobaiti

et al. 2021

Sensors

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The global COVID-19 pandemic that started in 2019 and created major disruptions around the world demonstrated the imperative need for quick, inexpensive, accessible and reliable diagnostic methods that would allow the detection of infected individuals with minimal resources. Radiography, and more specifically, chest radiography, is a relatively inexpensive medical imaging modality that can potentially offer a solution for the diagnosis of COVID-19 cases. In this work, we examined eleven deep convolutional neural network architectures for the task of classifying chest X-ray images as belonging to healthy individuals, individuals with COVID-19 or individuals with viral pneumonia. All the examined networks are established architectures that have been proven to be efficient in image classification tasks, and we evaluated three different adjustments to modify the architectures for the task at hand by expanding them with additional layers. The proposed approaches were evaluated for all the examined architectures on a dataset with real chest X-ray images, reaching the highest classification accuracy of 98.04% and the highest F1-score of 98.22% for the best-performing setting.

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Section: Introductionmentioning

confidence: 99%