CORONAVIRUS Diagnosis Based on Chest X-Ray Images and Pre-trained DenseNet-121

(1) * Yousra Kateb Mail (Faculty of Hydrocarbons and Chemistry, University of M’hamed Bougarra BOUMERDES, Algeria)
(2) Hocine Meglouli Mail (Faculty of Hydrocarbons and Chemistry, University of M’hamed Bougarra BOUMERDES, Algeria)
(3) Abdelmalek Khebli Mail (Faculty of Hydrocarbons and Chemistry, University of M’hamed Bougarra BOUMERDES, Algeria)
*corresponding author

Abstract


A serious global problem called COVID-19 has killed a great number of people and rendered many projects useless. The obtained individual's identification at the appropriate time is one of the crucial methods to reduce losses. By detecting and recognizing contaminated individuals in the early stages, artificial intelligence can help many associations in these situations. In this study, we offer a fully automated method to identify COVID-19 from a patient's chest X-ray images without the need for a clinical expert's assistance. A new dataset was released, which consists of 300 chest X-ray images from 100 healthy individuals, 100 individuals who were infected with Covid 19, and 100 images of viral pneumonitis. 100 more for testing, too. In order to attain an F1 score of 0.98, a Recall of 0.98, and also an Accuracy of 0.98 with this dataset, a classification method deep learning-based learning algorithm DenseNet-121, transfer learning, as well as data augmentation techniques were implemented. Therefore, even though there are not enough training photos, these findings are far better than other state-of-the-art.

Keywords


COVID-19 diagnosis; DenseNet-121; Chest X-Ray; Image Classification; Convolutional Neural Network

   

DOI

https://doi.org/10.31763/sitech.v2i2.779
      

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Copyright (c) 2021 Yousra Kateb, Hocine Meglouli, Abdelmalek Khebli

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Science in Information Technology Letters
ISSN 2722-4139
Published by Association for Scientific Computing Electrical and Engineering (ASCEE)
W : http://pubs2.ascee.org/index.php/sitech
E : andri@ascee.org, andri.pranolo.id@ieee.org

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0

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