School of Information Science and Engineering, Xinjiang University, Urumqi 830017, China.
Sensors (Basel). 2022 Nov 7;22(21):8578. doi: 10.3390/s22218578.
Recently, the COVID-19 pandemic coronavirus has put a lot of pressure on health systems around the world. One of the most common ways to detect COVID-19 is to use chest X-ray images, which have the advantage of being cheap and fast. However, in the early days of the COVID-19 outbreak, most studies applied pretrained convolutional neural network (CNN) models, and the features produced by the last convolutional layer were directly passed into the classification head. In this study, the proposed ensemble model consists of three lightweight networks, Xception, MobileNetV2 and NasNetMobile as three original feature extractors, and then three base classifiers are obtained by adding the coordinated attention module, LSTM and a new classification head to the original feature extractors. The classification results from the three base classifiers are then fused by a confidence fusion method. Three publicly available chest X-ray datasets for COVID-19 testing were considered, with ternary (COVID-19, normal and other pneumonia) and quaternary (COVID-19, normal) analyses performed on the first two datasets, bacterial pneumonia and viral pneumonia classification, and achieved high accuracy rates of 95.56% and 91.20%, respectively. The third dataset was used to compare the performance of the model compared to other models and the generalization ability on different datasets. We performed a thorough ablation study on the first dataset to understand the impact of each proposed component. Finally, we also performed visualizations. These saliency maps not only explain key prediction decisions of the model, but also help radiologists locate areas of infection. Through extensive experiments, it was finally found that the results obtained by the proposed method are comparable to the state-of-the-art methods.
最近，COVID-19 大流行冠状病毒给世界各地的卫生系统带来了很大压力。检测 COVID-19 的最常见方法之一是使用胸部 X 光图像，其优点是便宜且快速。然而，在 COVID-19 爆发的早期，大多数研究都应用了预先训练的卷积神经网络（CNN）模型，并且最后一层卷积层产生的特征直接被传递到分类头。在这项研究中，所提出的集成模型由三个轻量级网络组成，即 Xception、MobileNetV2 和 NasNetMobile，作为三个原始特征提取器，然后通过向原始特征提取器添加协调注意模块、LSTM 和新的分类头，获得三个基础分类器。然后通过置信度融合方法融合三个基础分类器的分类结果。考虑了三个用于 COVID-19 检测的公开胸部 X 射线数据集，对前两个数据集进行了三元（COVID-19、正常和其他肺炎）和四元（COVID-19、正常）分析，对细菌性肺炎和病毒性肺炎进行了分类，并分别达到了 95.56%和 91.20%的高准确率。第三个数据集用于比较模型与其他模型的性能以及在不同数据集上的泛化能力。我们在第一个数据集上进行了深入的消融研究，以了解每个提出的组件的影响。最后，我们还进行了可视化。这些显著图不仅解释了模型的关键预测决策，还帮助放射科医生定位感染区域。通过广泛的实验，最终发现所提出的方法的结果与最先进的方法相当。
