基于轻量级RG-DenseNet的COVID-19 CT图像分类

COVID-19 classification on CT image using lightweight RG DenseNet

目的:基于轻量级RG-DenseNet构建COVID-19 CT图像分类模型。方法:以DenseNet121为基础,添加通道和空间注意力机制模块减少无关特征的干扰,将DenseNet中的Bottleneck模块替换为前激活的RG-beneck2模块减少模型参数的同时保持精度尽可能不变。构建RG-DenseNet模型,在COVIDx CT-2A数据集上进行3分类实验。结果:RG-DenseNet准确率为98.93%、精确率为98.70%、召回率为98.97%、特异性为99.48%、F1分数为98.83%。结论:RG-DenseNet与原模型DenseNet121相比在保持准确度仅降低0.01%的情况下,减少92.7%的参数量和计算量,轻量化效果显著,具有实际应用价值。

Objective To construct a COVID-19 CT image classification model based on lightweight RG DenseNet.Methods A RG-DenseNet model was constructed by adding channel and spatial attention modules to DenseNet121 for minimizing the interference of irrelevant features,and replacing Bottleneck module in DenseNet with pre-activated RG beneck2 module for reducing model parameters while maintaining accuracy as much as possible.The model performance was verified with 3-category classification experiments on the COVIDx CT-2A dataset.Results RG-DenseNet had an accuracy,precision,recall rate,specificity,and F1-score of 98.93%,98.70%,98.97%,99.48%,and 98.83%,respectively.Conclusion Compared with the original model DenseNet121,RG-DenseNet reduces the number of parameters and the computational complexity by 92.7%,while maintaining an accuracy reduction of only 0.01%,demonstrating a significant lightweight effect and high practical application value.