Department of Computer Science, Shiraz University, Shiraz, Iran.
School of Computing and Communications, Lancaster University, Lancaster, UK.
Sci Rep. 2022 Feb 24;12(1):3116. doi: 10.1038/s41598-022-06802-7.
The rapid outbreak of coronavirus threatens humans' life all around the world. Due to the insufficient diagnostic infrastructures, developing an accurate, efficient, inexpensive, and quick diagnostic tool is of great importance. To date, researchers have proposed several detection models based on chest imaging analysis, primarily based on deep neural networks; however, none of which could achieve a reliable and highly sensitive performance yet. Therefore, the nature of this study is primary epidemiological research that aims to overcome the limitations mentioned above by proposing a large-scale publicly available dataset of chest computed tomography scan (CT-scan) images consisting of more than 13k samples. Secondly, we propose a more sensitive deep neural networks model for CT-scan images of the lungs, providing a pixel-wise attention layer on top of the high-level features extracted from the network. Moreover, the proposed model is extended through a transfer learning approach for being applicable in the case of chest X-Ray (CXR) images. The proposed model and its extension have been trained and evaluated through several experiments. The inclusion criteria were patients with suspected PE and positive real-time reverse-transcription polymerase chain reaction (RT-PCR) for SARS-CoV-2. The exclusion criteria were negative or inconclusive RT-PCR and other chest CT indications. Our model achieves an AUC score of 0.886, significantly better than its closest competitor, whose AUC is 0.843. Moreover, the obtained results on another commonly-used benchmark show an AUC of 0.899, outperforming related models. Additionally, the sensitivity of our model is 0.858, while that of its closest competitor is 0.81, explaining the efficiency of pixel-wise attention strategy in detecting coronavirus. Our promising results and the efficiency of the models imply that the proposed models can be considered reliable tools for assisting doctors in detecting coronavirus.
冠状病毒的迅速爆发威胁着全世界人类的生命。由于诊断基础设施不足，开发一种准确、高效、廉价和快速的诊断工具非常重要。迄今为止，研究人员已经提出了几种基于胸部成像分析的检测模型，主要基于深度神经网络；然而，没有一个能够达到可靠和高度敏感的性能。因此，本研究的性质主要是初步的流行病学研究，旨在通过提出一个由超过 13000 个样本组成的大规模公共胸部计算机断层扫描（CT）图像数据集来克服上述限制。其次，我们提出了一种更敏感的肺部 CT 扫描图像深度神经网络模型，在网络提取的高级特征之上提供像素级注意层。此外，通过迁移学习方法对提出的模型进行扩展，使其适用于胸部 X 射线（CXR）图像。所提出的模型及其扩展已经通过多项实验进行了训练和评估。纳入标准是疑似 PE 患者和 SARS-CoV-2 的实时逆转录聚合酶链反应（RT-PCR）阳性。排除标准是 RT-PCR 阴性或不确定以及其他胸部 CT 指征。我们的模型获得了 0.886 的 AUC 评分，明显优于其最接近的竞争对手，其 AUC 为 0.843。此外，在另一个常用基准上获得的结果显示 AUC 为 0.899，优于相关模型。此外，我们的模型的灵敏度为 0.858，而其最接近的竞争对手的灵敏度为 0.81，这解释了在检测冠状病毒时像素级注意策略的效率。我们有希望的结果和模型的效率表明，所提出的模型可以被认为是辅助医生检测冠状病毒的可靠工具。
