利用胸部x射线图像检测冠状病毒病的深度神经网络

R. Gupta, Nilesh Kunhare, R. K. Pateriya, Nikhlesh Pathik
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引用次数: 10

摘要

新型冠状病毒病(Covid-19)是2020年全球主要死亡原因之一,被世界卫生组织(WHO)宣布为大流行。这种病毒影响到世界上所有国家,截至2020年6月,有50万人死于Covid-19。由于该病毒具有高度传染性,因此早期发现对打破Covid链至关重要。中国最近的研究表明,胸部CT和x射线图像可以作为检测新冠病毒的初步测试。基于深度学习的CNN模型可用于从胸部x光图像中自动检测冠状病毒。本文提出了一种基于迁移学习的新冠肺炎检测方法。由于Covid胸部图像数量较少,我们使用预训练模型将x射线图像分为Covid和Normal类。本文对VGGNet-19、ResNet50和Inception_ResNet_V2等多种预训练模型进行了比较研究。实验结果表明,与VGGNet和ResNet模型相比,Inception_ResNet_V2模型的训练和测试准确率分别为99.26和94,得到了更好的结果。
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A Deep Neural Network for Detecting Coronavirus Disease Using Chest X-Ray Images
The novel Covid-19 is one of the leading cause of death worldwide in the year 2020 and declared as a pandemic by world health organization (WHO). This virus affecting all countries across the world and 5 lakh people die as of June 2020 due to Covid-19. Due to the highly contagious nature, early detection of this virus plays a vital role to break Covid chain. Recent studies done by China says that chest CT and X-Ray image may be used as a preliminary test for Covid detection. Deep learning-based CNN model can use to detect Coronavirus automatically from the chest X-rays images. This paper proposed a transfer learning-based approach to detect Covid disease. Due to the less number of Covid chest images, we are using a pre-trained model to classify X-ray images into Covid and Normal class. This paper presents the comparative study of a various pre-trained model like VGGNet-19, ResNet50 and Inception_ResNet_V2. Experiment results show that Inception_ResNet_V2 gives the better result as compare to VGGNet and ResNet model with training and test accuracy of 99.26 and 94, respectively.
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