论文标题
基于扫描图像的COVID-19自动诊断的综述
A Review of Automated Diagnosis of COVID-19 Based on Scanning Images
论文作者
论文摘要
Covid-19的大流行引起了数百万的感染，这在社会和经济上导致了全世界的巨大损失。由于假阴性速率和常规逆转录聚合酶链反应（RT-PCR）测试的时间耗时，因此基于X射线图像和计算机断层扫描（CT）图像进行了诊断。因此，计算机视觉领域的研究人员基于机器学习或深度学习开发了许多自动诊断模型，以帮助放射科医生并提高诊断准确性。在本文中，我们对这些最近出现的自动诊断模型进行了评论。从2020年2月14日至2020年7月21日提出的70款型号。我们从预处理，特征提取，分类和评估的角度分析了模型。基于现有模型的局限性，我们指出，转移学习和可解释性促进的域适应性将是未来的方向。
The pandemic of COVID-19 has caused millions of infections, which has led to a great loss all over the world, socially and economically. Due to the false-negative rate and the time-consuming of the conventional Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, diagnosing based on X-ray images and Computed Tomography (CT) images has been widely adopted. Therefore, researchers of the computer vision area have developed many automatic diagnosing models based on machine learning or deep learning to assist the radiologists and improve the diagnosing accuracy. In this paper, we present a review of these recently emerging automatic diagnosing models. 70 models proposed from February 14, 2020, to July 21, 2020, are involved. We analyzed the models from the perspective of preprocessing, feature extraction, classification, and evaluation. Based on the limitation of existing models, we pointed out that domain adaption in transfer learning and interpretability promotion would be the possible future directions.
