Bani Baker Qanita, Hammad Mahmoud, Al-Smadi Mohammed, Al-Jarrah Heba, Al-Hamouri Rahaf, Al-Zboon Sa'ad A
Faculty of Computer and Information Technology, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan.
Digital Learning and Online Education Office (DLOE), Qatar University, Doha 2713, Qatar.
J Imaging. 2024 Oct 13;10(10):250. doi: 10.3390/jimaging10100250.
The global spread of Coronavirus (COVID-19) has prompted imperative research into scalable and effective detection methods to curb its outbreak. The early diagnosis of COVID-19 patients has emerged as a pivotal strategy in mitigating the spread of the disease. Automated COVID-19 detection using Chest X-ray (CXR) imaging has significant potential for facilitating large-scale screening and epidemic control efforts. This paper introduces a novel approach that employs state-of-the-art Convolutional Neural Network models (CNNs) for accurate COVID-19 detection. The employed datasets each comprised 15,000 X-ray images. We addressed both binary (Normal vs. Abnormal) and multi-class (Normal, COVID-19, Pneumonia) classification tasks. Comprehensive evaluations were performed by utilizing six distinct CNN-based models (Xception, Inception-V3, ResNet50, VGG19, DenseNet201, and InceptionResNet-V2) for both tasks. As a result, the Xception model demonstrated exceptional performance, achieving 98.13% accuracy, 98.14% precision, 97.65% recall, and a 97.89% F1-score in binary classification, while in multi-classification it yielded 87.73% accuracy, 90.20% precision, 87.73% recall, and an 87.49% F1-score. Moreover, the other utilized models, such as ResNet50, demonstrated competitive performance compared with many recent works.
新型冠状病毒（COVID-19）的全球传播促使人们迫切开展有关可扩展且有效的检测方法的研究，以遏制其爆发。COVID-19患者的早期诊断已成为减轻疾病传播的关键策略。利用胸部X光（CXR）成像进行COVID-19的自动检测在促进大规模筛查和疫情防控工作方面具有巨大潜力。本文介绍了一种新颖的方法，该方法采用先进的卷积神经网络模型（CNN）进行准确的COVID-19检测。所使用的数据集每个都包含15,000张X光图像。我们处理了二分类（正常与异常）和多分类（正常、COVID-19、肺炎）任务。通过使用六种不同的基于CNN的模型（Xception、Inception-V3、ResNet50、VGG19、DenseNet201和InceptionResNet-V2）对这两项任务进行了全面评估。结果，Xception模型表现出色，在二分类中准确率达到98.13%，精确率达到98.14%，召回率达到97.65%，F1分数达到97.89%，而在多分类中准确率为87.73%，精确率为90.20%，召回率为87.73%，F1分数为87.49%。此外，其他使用的模型，如ResNet50，与许多近期研究相比表现出了有竞争力的性能。
