COVID-19 Recognition based on Deep Transfer Learning

Soulef Bouaafia, Seifeddine Messaoud, Randa Khemiri, Fatma Sayadi
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引用次数: 8

Abstract

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.
基于深度迁移学习的COVID-19识别
随着技术的快速发展,人工智能是最强大的技术,它在许多领域取得了很大的进步,包括计算机视觉和医学成像。本文提出了一种基于深度学习的COVID-19检测框架。提出了基于预训练深度卷积神经网络的深度迁移学习模型。几个预训练的模型,如DensNet201, InceptionV3, VGG16和ResNet50被评估用于该分析。本文中用于训练模型的数据集是两个不同类别的x射线和CT图像的混合:正常和COVID-19。实验结果表明,从测试精度的角度来看,DensNet201是最合适的深度迁移模型,在F1分数、精度和召回率等其他性能指标上,DensNet201达到了97%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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