Details

Autor(en) / Beteiligte

• Guefrechi, Sarra
• Jabra, Marwa Ben
• Ammar, Adel
• Koubaa, Anis
• Hamam, Habib

Titel

Deep learning based detection of COVID-19 from chest X-ray images

Ist Teil von

• Multimedia tools and applications, 2021-09, Vol.80 (21-23), p.31803-31820

Ort / Verlag

New York: Springer US

Erscheinungsjahr

2021

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• The whole world is facing a health crisis, that is unique in its kind, due to the COVID-19 pandemic. As the coronavirus continues spreading, researchers are concerned by providing or help provide solutions to save lives and to stop the pandemic outbreak. Among others, artificial intelligence (AI) has been adapted to address the challenges caused by pandemic. In this article, we design a deep learning system to extract features and detect COVID-19 from chest X-ray images. Three powerful networks, namely ResNet50, InceptionV3, and VGG16, have been fine-tuned on an enhanced dataset, which was constructed by collecting COVID-19 and normal chest X-ray images from different public databases. We applied data augmentation techniques to artificially generate a large number of chest X-ray images: Random Rotation with an angle between − 10 and 10 degrees, random noise, and horizontal flips. Experimental results are encouraging: the proposed models reached an accuracy of 97.20 % for Resnet50, 98.10 % for InceptionV3, and 98.30 % for VGG16 in classifying chest X-ray images as Normal or COVID-19. The results show that transfer learning is proven to be effective, showing strong performance and easy-to-deploy COVID-19 detection methods. This enables automatizing the process of analyzing X-ray images with high accuracy and it can also be used in cases where the materials and RT-PCR tests are limited.