基于DenseNet模型优化的新冠肺炎CT图像检测算法

COVID-19 CT Image Detection Based on Optimized DenseNet Model

提出了一种基于DenseNet模型优化的新冠肺炎CT图像检测算法。首先,分析并实现了ResNet、EfficientNet和DenseNet 3种网络模型,完成了在FlyAI平台上对新冠肺炎CT图像检测训练及分类任务。通过对技术原理、分类准确率等参量的分析和对比,表明DenseNet具有优越的自适应能力和分类能力。进一步针对该模型改用Focal Loss损失函数模型,在新冠肺炎CT图像数据集上的识别和分类达到94.57%的准确率,相比原来交叉熵损失函数下的模型,提升2.26%的精度,同时也高于其他检测方法,证明了所提算法的有效性。

This paper proposes a detection algorithm for COVID-19 CT image based on DenseNet model optimization.Firstly,three models are analyzed and implemented,which are ResNet,EfficientNet and DenseNet,tested on FlyAI platform.Through the analysis and comparison of their technical principles,classification accuracy and other parameters,we find that DenseNet has superior adaptive and classification capabilities.Secondly,the focus loss function model is used to improve this model.Its recognition and classification accuracy on the CT image data set of COVID-19 reaches 94.57%,which increased by 2.26%compared with the original model under the cross entropy loss function.At the same time,it is also higher than other detection methods,which proves the effectiveness of the proposed algorithm.