Systems and Information Department, National Research Centre, Dokki, 12311, Cairo, Egypt.
Sci Rep. 2024 May 21;14(1):11639. doi: 10.1038/s41598-024-61693-0.
COVID-19 is a kind of coronavirus that appeared in China in the Province of Wuhan in December 2019. The most significant influence of this virus is its very highly contagious characteristic which may lead to death. The standard diagnosis of COVID-19 is based on swabs from the throat and nose, their sensitivity is not high enough and so they are prone to errors. Early diagnosis of COVID-19 disease is important to provide the chance of quick isolation of the suspected cases and to decrease the opportunity of infection in healthy people. In this research, a framework for chest X-ray image classification tasks based on deep learning is proposed to help in early diagnosis of COVID-19. The proposed framework contains two phases which are the pre-processing phase and classification phase which uses pre-trained convolution neural network models based on transfer learning. In the pre-processing phase, different image enhancements have been applied to full and segmented X-ray images to improve the classification performance of the CNN models. Two CNN pre-trained models have been used for classification which are VGG19 and EfficientNetB0. From experimental results, the best model achieved a sensitivity of 0.96, specificity of 0.94, precision of 0.9412, F1 score of 0.9505 and accuracy of 0.95 using enhanced full X-ray images for binary classification of chest X-ray images into COVID-19 or normal with VGG19. The proposed framework is promising and achieved a classification accuracy of 0.935 for 4-class classification.
新型冠状病毒肺炎（Corona Virus Disease 2019，COVID-19），简称“新冠肺炎”，是指 2019 新型冠状病毒感染导致的肺炎。2019 年 12 月以来，湖北省武汉市部分医院陆续发现了多例有华南海鲜市场暴露史的不明原因肺炎病例，现已证实为 2019 新型冠状病毒感染引起的急性呼吸道传染病。该病毒传染性强，主要传播途径为经呼吸道飞沫和密切接触传播，在相对封闭的环境中长时间暴露于高浓度气溶胶情况下存在经气溶胶传播的可能，密闭、不通风场所可能存在气溶胶传播风险，其他传播途径尚待明确。人群普遍易感。老年人及有基础疾病者感染后病情较重，儿童及婴幼儿也有发病。其标准诊断是基于对咽喉和鼻腔拭子的检测，但这种方法的灵敏度不够高，因此容易出现错误。早期诊断 COVID-19 疾病对于提供快速隔离疑似病例的机会和减少健康人群的感染机会非常重要。在这项研究中，提出了一种基于深度学习的胸部 X 射线图像分类框架，以帮助早期诊断 COVID-19。所提出的框架包含两个阶段，即预处理阶段和分类阶段，该分类阶段使用基于迁移学习的预训练卷积神经网络模型。在预处理阶段，应用了不同的图像增强方法对全幅和分割的 X 射线图像进行增强，以提高 CNN 模型的分类性能。使用了两种经过预训练的 CNN 模型进行分类，分别是 VGG19 和 EfficientNetB0。从实验结果来看，使用增强后的全幅 X 射线图像对 COVID-19 或正常的胸部 X 射线图像进行二分类，使用 VGG19 最佳模型达到了 0.96 的敏感性、0.94 的特异性、0.9412 的精确性、0.9505 的 F1 分数和 0.95 的准确率。该框架具有很大的发展潜力，对于四分类任务，准确率也达到了 0.935。
