Radiologists versus Deep Convolutional Neural Networks: A Comparative Study for Diagnosing COVID-19

Autor: Mohammad Khalleel Sallam Ma'aitah, Ozum Tuncyurek, Abdulkader Helwan, Hani Hamdan, Dilber Uzun Ozsahin
Přispěvatelé: School of Engineering [Lebanese American University] (SOE/LAU), Lebanese American University (LAU), Near East University, Laboratoire des signaux et systèmes (L2S), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Jazyk: angličtina
Rok vydání: 2021
Předmět:
medicine.medical_specialty
Coronavirus disease 2019 (COVID-19)
Databases
Factual
Article Subject
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
Computer applications to medicine. Medical informatics
R858-859.7
02 engineering and technology
Convolutional neural network
General Biochemistry
Genetics and Molecular Biology
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
COVID-19 Testing
Deep Learning
Radiologists
0202 electrical engineering
electronic engineering
information engineering
medicine
Humans
[INFO]Computer Science [cs]
Diagnosis
Computer-Assisted
Diagnostic Errors
Expert Testimony
Lung
Pandemics
General Immunology and Microbiology
Artificial neural network
business.industry
SARS-CoV-2
Applied Mathematics
Deep learning
COVID-19
General Medicine
Mathematical Concepts
3. Good health
Modeling and Simulation
Test set
Deep neural networks
020201 artificial intelligence & image processing
Radiology
Artificial intelligence
Tomography
Neural Networks
Computer
business
Tomography
X-Ray Computed
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Research Article
Zdroj: Computational and Mathematical Methods in Medicine
Computational and Mathematical Methods in Medicine, 2021, pp.5527271. ⟨10.1155/2021/5527271⟩
Computational and Mathematical Methods in Medicine, Vol 2021 (2021)
ISSN: 1748-6718
DOI: 10.1155/2021/5527271⟩
Popis: International audience; The reverse transcriptase polymerase chain reaction (RT-PCR) is still the routinely used test for the diagnosis of SARS-CoV-2 (COVID-19). However, according to several reports, RT-PCR showed a low sensitivity and multiple tests may be required to rule out false negative results. Recently, chest computed tomography (CT) has been an efficient tool to diagnose COVID-19 as it is directly affecting the lungs. In this paper, we investigate the application of pre-trained models in diagnosing patients who are positive for COVID-19 and differentiating it from normal patients, who tested negative for coronavirus. The study aims to compare the generalization capabilities of deep learning models with two thoracic radiologists in diagnosing COVID-19 chest CT images. A dataset of 3000 images was obtained from the Near East Hospital, Cyprus, and used to train and to test the three employed pre-trained models. In a test set of 250 images used to evaluate the deep neural networks and the radiologists, it was found that deep networks (ResNet-18, ResNet-50, and DenseNet-201) can outperform the radiologists in terms of higher accuracy (97.8%), sensitivity (98.1%), specificity (97.3%), precision (98.4%), and F1-score (198.25%), in classifying COVID-19 images.
Databáze: OpenAIRE
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