[论文解读] COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images

Ezz El‐Din Hemdan, Marwa A. Shouman+1 more|arXiv (Cornell University)|Mar 24, 2020

COVID-19 diagnosis using AI参考文献 41被引用 972

一句话总结

引入 COVIDX-Net，一套由七种深度卷积神经网络架构组成的框架，使用小型数据集从胸部X光图像中分类 COVID-19；在普通（正常）与 COVID-19 类别上报告具有竞争力的 f1-score。

ABSTRACT

Background and Purpose: Coronaviruses (CoV) are perilous viruses that may cause Severe Acute Respiratory Syndrome (SARS-CoV), Middle East Respiratory Syndrome (MERS-CoV). The novel 2019 Coronavirus disease (COVID-19) was discovered as a novel disease pneumonia in the city of Wuhan, China at the end of 2019. Now, it becomes a Coronavirus outbreak around the world, the number of infected people and deaths are increasing rapidly every day according to the updated reports of the World Health Organization (WHO). Therefore, the aim of this article is to introduce a new deep learning framework; namely COVIDX-Net to assist radiologists to automatically diagnose COVID-19 in X-ray images. Materials and Methods: Due to the lack of public COVID-19 datasets, the study is validated on 50 Chest X-ray images with 25 confirmed positive COVID-19 cases. The COVIDX-Net includes seven different architectures of deep convolutional neural network models, such as modified Visual Geometry Group Network (VGG19) and the second version of Google MobileNet. Each deep neural network model is able to analyze the normalized intensities of the X-ray image to classify the patient status either negative or positive COVID-19 case. Results: Experiments and evaluation of the COVIDX-Net have been successfully done based on 80-20% of X-ray images for the model training and testing phases, respectively. The VGG19 and Dense Convolutional Network (DenseNet) models showed a good and similar performance of automated COVID-19 classification with f1-scores of 0.89 and 0.91 for normal and COVID-19, respectively. Conclusions: This study demonstrated the useful application of deep learning models to classify COVID-19 in X-ray images based on the proposed COVIDX-Net framework. Clinical studies are the next milestone of this research work.

研究动机与目标

• 促进基于胸部 X 线图像的自动 COVID-19 诊断，以辅助放射科医生。
• 提出一个框架（COVIDX-Net），使用多种深度 CNN 架构从 X 线图像中分类 COVID-19 状态。
• 在一个小型、早期的公开数据集上评估性能，并展示深度学习在此情境中的可行性。

提出的方法

• 在 COVIDX-Net 内探索七种 CNN 架构，包括修改后的 VGG19 和 MobileNet v2，作为分类器。
• 将归一化的图像强度用作分类的输入特征。
• 在 80% 的数据上进行训练，在 20% 上进行测试，以评估 COVID-19 检测性能。
• 提供一个框架，给定一张 X 线图像即可输出阴性与阳性 COVID-19 状态。

实验结果

研究问题

• RQ1 在数据有限的情况下，由多种 CNN 架构组成的框架是否能有效地将 COVID-19 从胸部 X 线图像中分类？
• RQ2 在 COVIDX-Net 中，哪些架构在 COVID-19 检测中实现了最佳的灵敏度与特异性平衡？
• RQ3 在可用数据集上，COVIDX-Net 在正常类与 COVID-19 类之间的性能对比如何？

主要发现

• 在可用数据集上，VGG19 和 DenseNet 变体在自动化 COVID-19 分类方面表现相似。
• 报告的 f1-score 为：正常类别 0.89，COVID-19 类别 0.91。
• 本研究证明在所提出的框架内，深度学习模型用于在 X 线图像中分类 COVID-19 的有效性。