DI-UMONS : Dépôt institutionnel de l’université de Mons

Recherche transversale
(titres de publication, de périodique et noms de colloque inclus)
2020-12-07 - Livre/Chapitre ou partie - Anglais - 22 page(s) (A publier)

Lessage Xavier, Mahmoudi Said , Mahmoudi Sidi , Laraba Sohaib , Debauche Olivier , Belarbi Mohammed Amin, "Chest X-ray images analysis with Deep Convolutional Neural Networks (CNN) for COVID-19 detection" in "Intelligent Healthcare Informatics for Fighting the COVID-19 and Other Pandemics and Epidemics"

  • Edition : Springer
  • Codes CREF : Sciences agronomiques (DI3600), Informatique générale (DI1162)
  • Unités de recherche UMONS : Informatique (F114)
  • Instituts UMONS : Institut de Recherche en Technologies de l’Information et Sciences de l’Informatique (InforTech), Institut NUMEDIART pour les Technologies des Arts Numériques (Numédiart)
Texte intégral :

Abstract(s) :

(Anglais) The coronavirus pandemic (COVID-19) is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The latest advances of deep learning and particularly convolutional neural networks (CNNs) have proven more than once their high accuracy in disease detection. In this chapter, we propose a deep learning-based approach for COVID-19 detection from chest X-ray images. The proposed approach applies, in an efficient way, the techniques of transfer learning and fine-tuning from pre-trained CNN models (InceptionV3, VGG16, MobileNet, EfficientNet, etc.). The dataset used for our experiments has three classes: normal, COVID-19, and other pathologies. The dataset is split into three sub-sets as follows: 70\% for training, 20\% for validation, and 10\% for the final test. To avoid underfitting or overfitting problems during the training process, we apply regularization techniques (L1 \& L2 regularizations, dropout, data augmentation, early stopping, cross-validation, etc.), which help in learning and providing a generalizable solution. As a result, we demonstrate the high efficiency of the proposed CNNs for the detection of COVID-19 from chest X-ray images. A comparison of different architectures shows that VGG16 and MobileNet provide the highest scores: 98.7\% and 99.3\% of accuracy respectively, 96.3\% and 98.7\% of sensitivity respectively, and 98.7\% of specificity for both models. The proposed solution is deployed in the cloud to provide high availability in real-time, thanks to a responsive website, and this without the need to download, install, and configure the required libraries.

Mots-clés :
  • (Anglais) classification
  • (Anglais) convolutional neural networks
  • (Anglais) Chest X-ray analysis