School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730000, China.
Center for Intelligent and Networked Systems (CFINS), Department of Automation, Tsinghua University, Beijing 100084, China.
J Healthc Eng. 2021 Aug 24;2021:6799202. doi: 10.1155/2021/6799202. eCollection 2021.
Most detection methods of coronavirus disease 2019 (COVID-19) use classic image classification models, which have problems of low recognition accuracy and inaccurate capture of modal features when detecting chest X-rays of COVID-19. This study proposes a COVID-19 detection method based on image modal feature fusion. This method first performs small-sample enhancement processing on chest X-rays, such as rotation, translation, and random transformation. Five classic pretraining models are used when extracting modal features. A global average pooling layer reduces training parameters and prevents overfitting. The model is trained and fine-tuned, the machine learning evaluation standard is used to evaluate the model, and the receiver operating characteristic (ROC) curve is drawn. Experiments show that compared with the classic model, the classification method in this study can more effectively detect COVID-19 image modal information, and it achieves the expected effect of accurately detecting cases.
大多数 2019 年冠状病毒病（COVID-19）的检测方法都使用经典的图像分类模型，这些模型在检测 COVID-19 的胸部 X 光片时存在识别准确率低和模态特征捕捉不准确的问题。本研究提出了一种基于图像模态特征融合的 COVID-19 检测方法。该方法首先对胸部 X 光片进行小样本增强处理，如旋转、平移和随机变换。在提取模态特征时使用了五个经典的预训练模型。全局平均池化层减少了训练参数，防止了过拟合。对模型进行训练和微调，使用机器学习评估标准来评估模型，并绘制接收者操作特征（ROC）曲线。实验表明，与经典模型相比，本研究中的分类方法能够更有效地检测 COVID-19 图像模态信息，达到了准确检测病例的预期效果。
