International Journal of Radiation Research
نشریه پرتو پژوه
Int J Radiat Res
Basic Sciences
http://ijrr.com
79
journal79
2322-3243
2345-4229
10.61186/ijrr
en
jalali
1400
1
1
gregorian
2021
4
1
19
2
online
1
fulltext
en
An accurate neural network algorithm to diagnose Covid-19 from CT images
Medical Physics
Medical Physics
تحقيق بديع
Original Research
<div style="text-align: justify;">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.</div>
Covid-19, chest ct images, SARS-CoV-2, neural network algorithm.
349
356
http://ijrr.com/browse.php?a_code=A-10-2188-4&slc_lang=en&sid=1
H.
Romdhane
romdhane.hamida@gmail.com
7900319475328460020103
7900319475328460020103
Yes
Université de Tunis El Manar, Laboratoire de recherche en Biophysique et Technologies Médicales (LRBTM), ISTMT
H.
Dziri
7900319475328460020104
7900319475328460020104
No
Université de Tunis El Manar, Laboratoire de recherche en Biophysique et Technologies Médicales (LRBTM), ISTMT
M.
Ali Cherni
7900319475328460020105
7900319475328460020105
No
Université de Tunis, LR13 ES03 SIME, ENSIT, Montfleury 1008 Tunisia
D.
Ben-Sellem
7900319475328460020106
7900319475328460020106
No
Université de Tunis El Manar, Laboratoire de recherche en Biophysique et Technologies Médicales (LRBTM), ISTMT