基于迁移学习和多参数融合优化的COVID-19辅助诊断模型

COVID-19 auxiliary diagnostic model based on migration learning and multi-parameter fusion optimization

目的 针对传统CT影像诊断准确性不高和效率低下问题,探讨深度学习技术在影像学中辅助诊断COVID-19的模型研究。方法 首先构建早期、进展期和重症期三类别的COVID-19影像学数据集,然后构建一个基于VGG-16迁移学习的诊断COVID-19的初始模型,最后通过逐步对全连接层网络结构、激活函数、损失函数、优化算法、学习率和样本批次大小的多参数融合优化,设计出一个COVID-19辅助诊断模型。结果 在COVID-19影像学测试集上COVID-19辅助诊断模型的准确率为98.10%,其中早期、进展期和重症期样本的敏感度分别为0.97、1.00、0.97,F1-score分别为0.98、0.97、0.99。结论 通过迁移学习和多参数融合优化策略,设计的COVID-19辅助诊断模型在测试集上有较高的准确率。在防控疫情时,辅助诊断模型能帮助医务工作者提高工作效率。

Objective To address the problem of low accuracy and efficiency of traditional CT image diagnosis, this paper discusses the model of deep learning technology in assisting the diagnosis of covid-19 in imaging. Methods First of all, a COVID-19 imaging dataset was constructed for three categories: early stage, progressive stage and critical stage. Then, an initial model for diagnosing COVID-19 based on VGG-16 transfer learning was constructed. Finally, a COVID-19 aided diagnosis model is designed by gradually optimizing the multi parameter fusion of the full connection layer network structure, activation function, loss function, optimization algorithm, learning rate and sample batch size. Results The accuracy of the COVID-19 auxiliary diagnosis model on the COVID-19 imaging test set was 98.10% with sensitivities of 0.97, 1.00 and0.97 for the early, progressive and severe samples, and F1-scores of 0.98, 0.97 and 0.99, respectively. Conclusion Through migration learning and multi-parameter fusion optimization strategies, the designed COVID-19 aided diagnosis model had high accuracy on the test set. The assisted diagnosis model can help medical workers to improve their efficiency when preventing and controlling epidemics.