[16] developed a deep learning model that can simultaneously localize the infectious regions of COVID-19 on chest X-ray images. Tang et al. [17] extracted radiomic features from CT images and then combined them with clinical indices for classification of severe vs. non-severe COVID-19 in ...
X-ray imagingDEEP learningCOVID-19LUNGSIMAGE processingCOMPUTED tomographyMEDICAL researchCOVID-19 is a highly contagious epidemic, and detection in the incipient phase is essential to curb the expansion of the disease. Chest Xrays are used in detecting COVID-19 infection...
Almost half (38/75, 50.7%) of the patients with normal chest x-ray were symptomatic and the majority (12/13, 92.3%) of patients with abnormal chest x-rays were symptomatic, there was a significant association between the chest x-ray findings and the symptoms (P = 0.005). Only one...
In their work, they have cut off the lungs from the x-ray images and have experimentally proved that a classifier can identify from which database the images came from. Thus, the authors highlight that joining different databases may add bias to the classification results, since the ...
Electronic records of the participants were retrieved for the patient’s demographics, clinical presentation, biochemical and radiological results, including those of chest X ray (CXR) and CT thorax (where applicable). We categorised COVID-19 into mild, moderate, severe and critical illness as per...
COVID-19 is a new infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS CoV-2). Since the outbreak in December 2019, it has caused an unprecedented world pandemic, leading to a global human health crisis. Although SARS CoV-2 mainly affects the lungs, causing...
2020). Though the confinement of human populations to their homes, throughout the world, resulted in an increase of household energy consumption, an overall net electricity decline was observed due to the plummeted demand of industrial electricity (Abu-Rayash and Dincer 2020). A dramatic plunge ...
In this study, we propose a two-stage workflow used for the segmentation and scoring of lung diseases. The workflow inherits quantification, qualification, and visual assessment of lung diseases on X-ray images estimated by radiologists and clinicians. I
The proposed algorithm was trained and tested using 40 contrast-enhanced lungs X-ray images of size 512× 512 in-plane resolution. This dataset includes 15 normal lung images and 25 infected lungs with COVID-19 images from the Montgomery County X-ray Set and covid-chest X-ray-dataset-...
Chest X-ray Cardiac Troponins Natriuretic Peptides (BNP or NT-proBNP) Echocardiography Complications of Cardiac Injury in COVID-19 Arrhythmias and Conduction Abnormalities Although no specific arrhythmia has been linked to SARS-CoV-2 infection, both brady- and tachyarrhythmias, as well as sudden car...