论文标题
COVID-19：使用卷积神经网络的转移学习从X射线图像中自动检测
Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks
论文作者
论文摘要
在这项研究中，使用常见肺炎，Covid-19和正常事件的患者的X射线图像数据集用于自动检测冠状病毒。该研究的目的是评估近年来针对医学图像分类提议的最先进的卷积神经网络体系结构的性能。具体而言，采用了称为转移学习的程序。通过转移学习，小型医学图像数据集中各种异常的检测是一个可实现的目标，通常会产生显着的结果。该实验中使用的数据集是1427 X射线图像的集合。 224张带有确认的COVID-19的图像，包括确认的常见肺炎的700张图像，以及504张正常情况的图像。数据是从公共医疗存储库上可用的X射线图像中收集的。通过转移学习，在检测Covid-19的总体准确度中达到了97.82％。
In this study, a dataset of X-Ray images from patients with common pneumonia, Covid-19, and normal incidents was utilized for the automatic detection of the Coronavirus. The aim of the study is to evaluate the performance of state-of-the-art Convolutional Neural Network architectures proposed over recent years for medical image classification. Specifically, the procedure called transfer learning was adopted. With transfer learning, the detection of various abnormalities in small medical image datasets is an achievable target, often yielding remarkable results. The dataset utilized in this experiment is a collection of 1427 X-Ray images. 224 images with confirmed Covid-19, 700 images with confirmed common pneumonia, and 504 images of normal conditions are included. The data was collected from the available X-Ray images on public medical repositories. With transfer learning, an overall accuracy of 97.82% in the detection of Covid-19 is achieved.
