publication . 2021

Chest X-ray Images Analysis with Deep Convolutional Neural Networks (CNN) for COVID-19 Detection

Xavier Lessage; Saïd Mahmoudi; Sidi Ahmed Mahmdoudi; Sohaib Laraba; Olivier Debauche; Mohammed Amin Belarbi;
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  • Published: 15 Mar 2021
  • Publisher: Springer International Publishing
  • Country: Belgium
Abstract
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 new 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, 15% for validation, and 15% 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: 97.5% and 99.3% of accuracy respectively, 98.7% and 99.3% of sensitivity respectively. In addition, both models provide the scores of 96.3% and 99.2% respectively for specificity. 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. © 2022, Springer Nature Switzerland AG.
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free text keywords: Engineering, computing & technology, Computer science, Ingénierie, informatique & technologie, Sciences informatiques, Regularization (mathematics), Transfer of learning, Sensitivity (control systems), Artificial intelligence, business.industry, business, Computer science, Dropout (neural networks), Overfitting, Deep learning, Pattern recognition, Early stopping, Convolutional neural network
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