Lessage Xavier, Mahmoudi Saïd, Mahmoudi Sidi Ahmed, Sohaib Laraba, Olivier Debauche, Mohammed Amin Belarbi
University of Mons, Computer Sciences, Mons, Belgium
Date: 11 février 2021
Publication: Publications scientifiques ⊕
Ingénierie et science des données ⊕
Domaine: Santé ⊕
Abstract : The latest advances in machine learning and in particular with convolutional neurons (CNN) have proven more than once their great accuracy in the detection of diseases. In this paper, we present a new approach for COVID-19 detection from chest X-ray images using Deep Learning algorithms. An efficient process consisting of techniques of transfer learning and a fine-tuning from pre-trained CNN models (InceptionV3, VGG16, MobileNet, EfficientNet, etc.) is proposed. A comparison of different architectures shows that VGG16 and MobileNet provide the highest scores : 97.5% and 99.3% of accuracy. Experimentations have been conducted using an anonymized database from an Italian hospital thanks to a retrospective study.
Keywords : x-ray analysis, classification, convolutional neural networks, covid-19.
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