利用CNN-XGBoost深度网络检测胸部x线图像中的COVID-19

Ahmed Mabrouk Fangoh, Sahar Selim
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引用次数: 4

摘要

在撰写本文时,COVID-19大流行是全球主要死亡原因之一,并给每个人的生活带来了重大变化。虽然仍然没有疫苗,但早期筛查和发现疾病可以极大地帮助管理卫生保健系统的能力,并使放射科医生和临床医生能够更好地分配他们的优先事项。随着深度学习在过去几年的快速发展,它在解决这个问题上的应用是很自然的。本文旨在概述使用深度学习对COVID-19病例进行分类的几个主要进展,说明常见技术和面临的问题。在此之后,提出并测试了一个深度学习架构,然后与上述论文的发现进行了比较。
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Using CNN-XGBoost Deep Networks for COVID-19 Detection in Chest X-ray Images
At the time of writing, the COVID-19 pandemic is one of the lead causes of death worldwide and has caused significant changes to everyone’s lives. While a vaccine is still unavailable, early screenings and detection of the disease can significantly help in managing the healthcare system’s capacity as well as allow radiologists and clinicians better assign their priorities. With deep learning’s rapid advancements over the last few years, its application in solving this issue is only natural. This paper aims to outline the works of a few major developments in the field of using deep learning to classify COVID-19 cases, illustrating common techniques and issues faced. Following this, a deep learning architecture is proposed and tested, then compared to the findings of the mentioned papers.
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