Covid-19 Classification Using Deep Learning in Chest X-Ray Images

Z. Karhan, F. Akal
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引用次数: 29

Abstract

Covid-19 virus, which has emerged in the Republic of China in an undetermined cause, has affected the whole world quickly. It is important to detect positive cases early to prevent further spread of the outbreak. In the diagnostic phase, radiological images of the chest are determinative as well as the RT-PCR (Reverse Transcription-Polymerase Chain Reaction) test. It was classified with the ResNet50 model, which is a convolutional neural network architecture in Covid-19 detection using chest x-ray images. Chest X-Ray image analysis can be done and infected individuals can be identified thanks to artificial intelligence quickly. The experimental results are encouraging in terms of the use of computer-aided in the field of pathology. It can also be used in situations where the possibilities and RT-PCR tests are insufficient.
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在胸部x射线图像中使用深度学习进行Covid-19分类
新冠肺炎疫情在中华民国出现,原因不明,迅速波及全球。重要的是及早发现阳性病例,以防止疫情进一步蔓延。在诊断阶段,胸部放射图像和RT-PCR(逆转录聚合酶链反应)测试是决定性的。它被归类为ResNet50模型,这是一种利用胸部x射线图像检测新冠病毒的卷积神经网络架构。借助人工智能,可以快速进行胸部x光图像分析,并识别出感染者。在病理学领域使用计算机辅助方面,实验结果令人鼓舞。它也可用于可能性和RT-PCR检测不足的情况。
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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