Author: Bouaafia, S.; Messaoud, S.; Khemiri, R.; Sayadi, F. E.
Title: COVID-19 Recognition based on Deep Transfer Learning Cord-id: 7zggte8j Document date: 2021_1_1
ID: 7zggte8j
Snippet: With the rapid development technology, Artificial Intelligence is the most powerful technique, it has made great progress in many areas, including computer vision and medical imaging. This paper proposes a deep learning-based framework for COVID-19 detection. Deep transfer learning models-based on a pre-trained Deep convolutional Neural Network are proposed. Several pre-trained models, such as DensNet201, InceptionV3, VGG16, and ResNet50 were evaluated for this analysis.The datasets used in this
Document: With the rapid development technology, Artificial Intelligence is the most powerful technique, it has made great progress in many areas, including computer vision and medical imaging. This paper proposes a deep learning-based framework for COVID-19 detection. Deep transfer learning models-based on a pre-trained Deep convolutional Neural Network are proposed. Several pre-trained models, such as DensNet201, InceptionV3, VGG16, and ResNet50 were evaluated for this analysis.The datasets used in this paper for training model are a mix of X-ray and CT images in two distinct categories: Normal and COVID-19. The experimental results proved that the DensNet201 was the most suitable deep transfer model according to the test accuracy measure and that it reached 97% with the other performance metrics such as F1 score, precision, and recall. © 2021 IEEE.
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