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:: Volume 19, Issue 2 (4-2021) ::
Int J Radiat Res 2021, 19(2): 349-356 Back to browse issues page
An accurate neural network algorithm to diagnose Covid-19 from CT images
H. Romdhane PhD., , H. Dziri, M. Ali Cherni, D. Ben-Sellem
Université de Tunis El Manar, Laboratoire de recherche en Biophysique et Technologies Médicales (LRBTM), ISTMT , romdhane.hamida@gmail.com
Abstract:   (166 Views)
Background: A new coronavirus appeared in late December 2019 in Wuhan, China. He was named Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). This virus is responsible for Covid-19, the name given to the disease associated with it. It spreads worldwide, infecting more than a million people and killing more than 70 miles. The rapid and accurate diagnosis of suspected Covid-19 cases plays a crucial role in medical treatment and timely quarantine. Materials and Methods: In order to counter the Covid-19 pandemic, we have developed a method for the automatic detection of Covid-19, from 2D computed tomography (CT) chest images. It is a supervised software system based on the ANN (Artificial Neural Network) algorithm. Pulmonary CT images were collected from multiple international datasets, with a total of 395 images: 70% were used for training and 30% were used for testing. For each patient, the lungs were segmented using simple thresholding. Then, the segmented lungs were fed into a neural network to predict the probability of SARS-CoV-2 infectious. Results: The internal validation achieved a total accuracy of 97.5% with a specificity of 96.6 % and a 100 % sensitivity. Conclusion: These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate Covid-19 diagnosis.
Keywords: Covid-19, chest ct images, SARS-CoV-2, neural network algorithm.
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Type of Study: Original Research | Subject: Medical Physics
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Romdhane H, Dziri H, Ali Cherni M, Ben-Sellem D. An accurate neural network algorithm to diagnose Covid-19 from CT images. Int J Radiat Res. 2021; 19 (2) :349-356
URL: http://ijrr.com/article-1-3658-en.html

Volume 19, Issue 2 (4-2021) Back to browse issues page
International Journal of Radiation Research
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