An Automated Approach for Timely Diagnosis and Prognosis of Coronavirus Disease
Authors: Ali, A.R. and Budka, M.
Journal: Proceedings of the International Joint Conference on Neural Networks
Volume: 2021-July
DOI: 10.1109/IJCNN52387.2021.9533786
Abstract:Since the outbreak of Coronavirus Disease 2019 (COVID-19), most of the impacted patients have been diagnosed with high fever, dry cough, and soar throat leading to severe pneumonia. Hence, to date, the diagnosis of COVID-19 from lung imaging is proved to be a major evidence for early diagnosis of the disease. Although nucleic acid detection using real-time reverse-transcriptase polymerase chain reaction (rRT-PCR) remains a gold standard for the detection of COVID-19, the proposed approach focuses on the automated diagnosis and prognosis of the disease from a non-contrast chest computed tomography (CT) scan for timely diagnosis and triage of the patient. The prognosis covers the quantification and assessment of the disease to help hospitals with the management and planning of crucial resources, such as medical staff, ventilators and intensive care units (ICUs) capacity. The approach utilises deep learning techniques for automated quantification of severity of COVID-19 disease via measuring the area of multiple rounded ground-glass opacities (GGO) and consolidations in the periphery (CP) of the lungs and accumulating them to form a severity score. The severity of the disease can be correlated with the medicines prescribed during the triage to assess the effectiveness of the treatment. The proposed approach shows promising results where the classification model achieved 93% accuracy on hold-out data.
https://eprints.bournemouth.ac.uk/35476/
Source: Scopus
An Automated Approach for Timely Diagnosis and Prognosis of Coronavirus Disease
Authors: Ali, A.R. and Budka, M.
Journal: 2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
ISSN: 2161-4393
DOI: 10.1109/IJCNN52387.2021.9533786
https://eprints.bournemouth.ac.uk/35476/
Source: Web of Science (Lite)
An Automated Approach for Timely Diagnosis and Prognosis of Coronavirus Disease.
Authors: Ali, A.R. and Budka, M.
Journal: CoRR
Volume: abs/2104.14116
https://eprints.bournemouth.ac.uk/35476/
Source: DBLP
An Automated Approach for Timely Diagnosis and Prognosis of Coronavirus Disease
Authors: Ali, A.R. and Budka, M.
Journal: 2021 International Joint Conference on Neural Networks (IJCNN)
Abstract:Since the outbreak of Coronavirus Disease 2019 (COVID-19), most of the impacted patients have been diagnosed with high fever, dry cough, and soar throat leading to severe pneumonia. Hence, to date, the diagnosis of COVID-19 from lung imaging is proved to be a major evidence for early diagnosis of the disease. Although nucleic acid detection using real-time reverse-transcriptase polymerase chain reaction (rRT-PCR) remains a gold standard for the detection of COVID-19, the proposed approach focuses on the automated diagnosis and prognosis of the disease from a non-contrast chest computed tomography (CT)scan for timely diagnosis and triage of the patient. The prognosis covers the quantification and assessment of the disease to help hospitals with the management and planning of crucial resources, such as medical staff, ventilators and intensive care units (ICUs) capacity. The approach utilises deep learning techniques for automated quantification of the severity of COVID-19 disease via measuring the area of multiple rounded ground-glass opacities (GGO) and consolidations in the periphery (CP) of the lungs and accumulating them to form a severity score. The severity of the disease can be correlated with the medicines prescribed during the triage to assess the effectiveness of the treatment. The proposed approach shows promising results where the classification model achieved 93% accuracy on hold-out data.
https://eprints.bournemouth.ac.uk/35476/
http://dx.doi.org/10.1109/IJCNN52387.2021.9533786
Source: arXiv
An Automated Approach for Timely Diagnosis and Prognosis of Coronavirus Disease
Authors: Ali, A.R. and Budka, M.
Abstract:Since the outbreak of Coronavirus Disease 2019 (COVID-19), most of the impacted patients have been diagnosed with high fever, dry cough, and soar throat leading to severe pneumonia. Hence, to date, the diagnosis of COVID-19 from lung imaging is proved to be a major evidence for early diagnosis of the disease. Although nucleic acid detection using real-time reverse-transcriptase polymerase chain reaction (rRT-PCR) remains a gold standard for the detection of COVID-19, the proposed approach focuses on the automated diagnosis and prognosis of the disease from a non-contrast chest computed tomography (CT)scan for timely diagnosis and triage of the patient. The prognosis covers the quantification and assessment of the disease to help hospitals with the management and planning of crucial resources, such as medical staff, ventilators and intensive care units (ICUs) capacity. The approach utilises deep learning techniques for automated quantification of the severity of COVID-19 disease via measuring the area of multiple rounded ground-glass opacities (GGO) and consolidations in the periphery (CP) of the lungs and accumulating them to form a severity score. The severity of the disease can be correlated with the medicines prescribed during the triage to assess the effectiveness of the treatment. The proposed approach shows promising results where the classification model achieved 93% accuracy on hold-out data.
https://eprints.bournemouth.ac.uk/35476/
Source: BURO EPrints