论文标题
通过CT和X射线对肺部病变的检测和分类进行调查
Survey of the Detection and Classification of Pulmonary Lesions via CT and X-Ray
论文作者
论文摘要
近年来，几种肺部疾病的患病率，尤其是2019年冠状病毒病（Covid-19）大流行，引起了全球关注。这些疾病可以在肺成像的帮助下有效地诊断和治疗。随着深度学习技术的发展和许多公共医学图像数据集的出现，通过医学成像诊断肺部疾病的诊断得到了进一步改善。本文回顾了过去十年中肺CT和X射线图像检测和分类。它还概述了基于各种病变的成像特征的肺结节，肺炎和其他常见的肺部病变的检测。此外，这篇评论介绍了26个常用的公共医疗图像数据集，总结了最新技术，并讨论了当前的挑战和未来的研究方向。
In recent years, the prevalence of several pulmonary diseases, especially the coronavirus disease 2019 (COVID-19) pandemic, has attracted worldwide attention. These diseases can be effectively diagnosed and treated with the help of lung imaging. With the development of deep learning technology and the emergence of many public medical image datasets, the diagnosis of lung diseases via medical imaging has been further improved. This article reviews pulmonary CT and X-ray image detection and classification in the last decade. It also provides an overview of the detection of lung nodules, pneumonia, and other common lung lesions based on the imaging characteristics of various lesions. Furthermore, this review introduces 26 commonly used public medical image datasets, summarizes the latest technology, and discusses current challenges and future research directions.
