Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks
Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks
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DOI：
10.1007/s13246-020-00865-4
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发表时间：
2020-06-01
影响因子：
4.4
通讯作者：
Mpesiana, Tzani A.
中科院分区：
文献类型：
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作者：
Apostolopoulos, Ioannis D.;Mpesiana, Tzani A.
本研究利用普通细菌性肺炎、确诊Covid-19疾病和正常事件患者的x射线图像数据集，自动检测冠状病毒疾病。本研究的目的是评估近年来提出的用于医学图像分类的最先进的卷积神经网络架构的性能。具体来说，我们采用了迁移学习的方法。通过迁移学习，检测小型医学图像数据集中的各种异常是一个可以实现的目标，通常会产生显着的结果。本实验使用的数据集有两个。首先，收集1427张x射线图像，其中确诊新冠肺炎图像224张，确诊普通细菌性肺炎图像700张，正常图像504张。其次，该数据集包括224张新冠肺炎确诊图像、714张细菌性和病毒性肺炎确诊图像和504张正常图像。数据是从公共医疗库中现有的x射线图像中收集的。结果表明，结合x射线成像的深度学习可以提取出与新冠肺炎相关的重要生物标志物，获得的最佳准确率、灵敏度和特异性分别为96.78%、98.66%和96.46%。由于到目前为止，所有诊断测试都显示出令人担忧的失败率，因此医学界可以根据研究结果评估将x射线纳入疾病诊断的可能性，同时可以进行更多的研究，从不同方面评估x射线方法。
In this study, a dataset of X-ray images from patients with common bacterial pneumonia, confirmed Covid-19 disease, and normal incidents, was utilized for the automatic detection of the Coronavirus disease. The aim of the study is to evaluate the performance of state-of-the-art convolutional neural network architectures proposed over the 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 datasets utilized in this experiment are two. Firstly, a collection of 1427 X-ray images including 224 images with confirmed Covid-19 disease, 700 images with confirmed common bacterial pneumonia, and 504 images of normal conditions. Secondly, a dataset including 224 images with confirmed Covid-19 disease, 714 images with confirmed bacterial and viral pneumonia, and 504 images of normal conditions. The data was collected from the available X-ray images on public medical repositories. The results suggest that Deep Learning with X-ray imaging may extract significant biomarkers related to the Covid-19 disease, while the best accuracy, sensitivity, and specificity obtained is 96.78%, 98.66%, and 96.46% respectively. Since by now, all diagnostic tests show failure rates such as to raise concerns, the probability of incorporating X-rays into the diagnosis of the disease could be assessed by the medical community, based on the findings, while more research to evaluate the X-ray approach from different aspects may be conducted.