Lee Chin Poo, Lim Kian Ming
Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia.
Diagnostics (Basel). 2022 Jul 29;12(8):1828. doi: 10.3390/diagnostics12081828.
The COVID-19 pandemic has caused a devastating impact on the social activity, economy and politics worldwide. Techniques to diagnose COVID-19 cases by examining anomalies in chest X-ray images are urgently needed. Inspired by the success of deep learning in various tasks, this paper evaluates the performance of four deep neural networks in detecting COVID-19 patients from their chest radiographs. The deep neural networks studied include VGG16, MobileNet, ResNet50 and DenseNet201. Preliminary experiments show that all deep neural networks perform promisingly, while DenseNet201 outshines other models. Nevertheless, the sensitivity rates of the models are below expectations, which can be attributed to several factors: limited publicly available COVID-19 images, imbalanced sample size for the COVID-19 class and non-COVID-19 class, overfitting or underfitting of the deep neural networks and that the feature extraction of pre-trained models does not adapt well to the COVID-19 detection task. To address these factors, several enhancements are proposed, including data augmentation, adjusted class weights, early stopping and fine-tuning, to improve the performance. Empirical results on DenseNet201 with these enhancements demonstrate outstanding performance with an accuracy of 0.999%, precision of 0.9899%, sensitivity of 0.98%, specificity of 0.9997% and F1-score of 0.9849% on the COVID-Xray-5k dataset.
新冠疫情对全球社会活动、经济和政治造成了毁灭性影响。迫切需要通过检查胸部X光图像中的异常来诊断新冠病例的技术。受深度学习在各项任务中取得成功的启发，本文评估了四种深度神经网络从胸部X光片检测新冠患者的性能。所研究的深度神经网络包括VGG16、MobileNet、ResNet50和DenseNet201。初步实验表明，所有深度神经网络都表现出良好的前景，而DenseNet201比其他模型更出色。然而，这些模型的灵敏度低于预期，这可归因于几个因素：公开可用的新冠图像有限、新冠类别和非新冠类别样本大小不均衡、深度神经网络的过拟合或欠拟合，以及预训练模型的特征提取不能很好地适应新冠检测任务。为解决这些因素，提出了几种改进方法，包括数据增强、调整类别权重、提前停止和微调，以提高性能。在DenseNet201上采用这些改进方法的实证结果表明，在COVID-Xray-5k数据集上，其准确率为0.999%、精确率为0.9899%、灵敏度为0.98%、特异性为0.9997%、F1分数为0.9849%，表现出色。
