基于深度迁移学习的COVID-19识别

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

摘要

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。
COVID-19 Recognition based on Deep Transfer Learning
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.
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