COVID-19 is an ongoing pandemic disease. To make more accurate diagnosis on COVID-19 than existing approaches, this paper proposed a novel method combining DenseNet and optimization of transfer learning setting (OTLS) strategy. Preprocessing was used to enhance, crop, and resize the collected chest CT images. Data augmentation method was used to increase the size of training set. A composite learning factor (CLF) was employed which assigned different learning factor to three types of layers: frozen layers, middle layers, and new layers. Meanwhile, the OTLS strategy was proposed. Finally, precomputation method was utilized to reduce RAM storage and accelerate the algorithm. We observed that optimization setting “201-IV” can achieve the best performance by proposed OTLS strategy. The sensitivity, specificity, precision, and accuracy of our proposed method were 96.35 ± 1.07, 96.25 ± 1.16, 96.29 ± 1.11, and 96.30 ± 0.56, respectively. The proposed DenseNet-OTLS method achieved better performances than state-of-the-art approaches in diagnosing COVID-19.

COVID-19是一种持续的大流行疾病。为了比现有方法对COVID-19进行更准确的诊断，本文提出了一种结合DenseNet和转移学习设置（OTLS）策略优化的新方法。预处理用于增强，裁剪和调整收集的胸部CT图像的大小。数据扩充方法用于增加训练集的大小。使用了复合学习因子（CLF），它为三种类型的层分配了不同的学习因子：冻结层，中间层和新层。同时，提出了OTLS策略。最后，采用预计算方法来减少RAM存储空间并加速算法。我们观察到优化设置“ 201-IV”可以通过提出的OTLS策略获得最佳性能。灵敏度，特异性，精密度，我们的方法的准确性和准确性分别为96.35±1.07、96.25±1.16、96.29±1.11和96.30±0.56。提出的DenseNet-OTLS方法在诊断COVID-19方面比最先进的方法具有更好的性能。