论文标题
COVID-19检测的人工智能 - 最先进的评论
Artificial Intelligence for COVID-19 Detection -- A state-of-the-art review
论文作者
论文摘要
Covid-19的出现需要科学界为其适当的管理做出许多努力。面对大流行造成的无休止的破坏，需要紧急临床反应。这些努力包括用于改进筛查，治疗，疫苗开发，接触跟踪和生存预测的技术创新。可以在所有上述领域中寻求深度学习（DL）和人工智能（AI）的使用。本文旨在回顾深度学习和人工智能在整体Covid-19管理的各个方面的作用，尤其是对于COVID-19的检测和分类。开发了DL模型来分析患者的CT扫描和X射线图像，并预测其病理状况。 DL模型旨在检测COVID-19肺炎，分类和区分COVID-19，社区获得的肺炎（CAP），病毒和细菌性肺炎以及正常情况。此外，可以构建复杂的模型以细分肺部的受影响区域并量化感染量，以更好地理解损害程度。许多模型是独立开发的，或者借助于预先训练的模型，例如VGG19，RESNET50和Alexnet利用转移学习的概念。除模型开发外，还进行了数据预处理和增强，以应对医疗应用中经常遇到的数据样本不足的挑战。可以评估可以有效地实施DL和AI，以承受全球紧急事件的挑战
The emergence of COVID-19 has necessitated many efforts by the scientific community for its proper management. An urgent clinical reaction is required in the face of the unending devastation being caused by the pandemic. These efforts include technological innovations for improvement in screening, treatment, vaccine development, contact tracing and, survival prediction. The use of Deep Learning (DL) and Artificial Intelligence (AI) can be sought in all of the above-mentioned spheres. This paper aims to review the role of Deep Learning and Artificial intelligence in various aspects of the overall COVID-19 management and particularly for COVID-19 detection and classification. The DL models are developed to analyze clinical modalities like CT scans and X-Ray images of patients and predict their pathological condition. A DL model aims to detect the COVID-19 pneumonia, classify and distinguish between COVID-19, Community-Acquired Pneumonia (CAP), Viral and Bacterial pneumonia, and normal conditions. Furthermore, sophisticated models can be built to segment the affected area in the lungs and quantify the infection volume for a better understanding of the extent of damage. Many models have been developed either independently or with the help of pre-trained models like VGG19, ResNet50, and AlexNet leveraging the concept of transfer learning. Apart from model development, data preprocessing and augmentation are also performed to cope with the challenge of insufficient data samples often encountered in medical applications. It can be evaluated that DL and AI can be effectively implemented to withstand the challenges posed by the global emergency
