基于ResNet50-OC模型的彩色眼底照片质量多分类方法效果评估
Evaluation of multi-classification method of color fundus photograph quality based on ResNet50-OC
目的:对基于深度学习的ResNet50-OC模型彩色眼底照片质量多分类的效果进行评估。方法:纳入2018年7月在南京医科大学附属明基医院收集的彩色眼底照片PD数据集及EyePACS数据集,临床医师根据眼底图像的成像质量将其大致分为质量较好、曝光不足、曝光过度、边缘模糊和镜头反光5类。在训练集中,每个类别包含1 000张图像,其中800张选自EyePACS数据集,200张选自PD数据集;在测试集中,每个类别包含500张图像,其中400张选自EyePACS数据集,100张选自PD数据集。训练集总计5 000张图像,测试集总计2 500张图像。对图像进行归一化处理和数据扩增。采用迁移学习方法初始化网络模型的参数,在此基础上对比当前深度学习主流分类网络VGG、Inception-resnet-v2、ResNet和DenseNet,选取准确率和Micro F1值最优的网络ResNet50作为分类模型的主网络。在ResNet50训练过程中引入One-Cycle策略加快模型收敛速度,得到最优模型ResNet50-OC并将其应用于眼底照片质量多分类,评估ResNet50与ResNet50-OC对眼底照片进行多分类的准确率和Micro F1值。结果:ResNet50对彩色眼底照片质量多分类准确率和Micro F1值明显高于VGG、Inception-resnet-v2、ResNet34和DenseNet。ResNet50-OC模型训练15轮对眼底图像质量多分类准确率为98.77%,高于ResNet50训练50轮的98.76%;ResNet50-OC模型训练15轮对眼底图像质量多分类的Micro F1值为98.78%,与ResNet50训练50轮的Micro F1值相同。结论:ResNet50-OC模型可以准确、有效地对彩色眼底照片质量进行多分类,One-Cycle策略可减少训练次数,提高分类效率。
更多Objective:To evaluate the efficiency of ResNet50-OC model based on deep learning for multiple classification of color fundus photographs.Methods:The proprietary dataset (PD) collected in July 2018 in BenQ Hospital of Nanjing Medical University and EyePACS dataset were included.The included images were classified into five types of high quality, underexposure, overexposure, blurred edges and lens flare according to clinical ophthalmologists.There were 1 000 images (800 from EyePACS and 200 from PD) for each type in the training dataset and 500 images (400 from EyePACS and 100 from PD) for each type in the testing dataset.There were 5 000 images in the training dataset and 2 500 images in the testing dataset.All images were normalized and augmented.The transfer learning method was used to initialize the parameters of the network model, on the basis of which the current mainstream deep learning classification networks (VGG, Inception-resnet-v2, ResNet, DenseNet) were compared.The optimal network ResNet50 with best accuracy and Micro F1 value was selected as the main network of the classification model in this study.In the training process, the One-Cycle strategy was introduced to accelerate the model convergence speed to obtain the optimal model ResNet50-OC.ResNet50-OC was applied to multi-class classification of fundus image quality.The accuracy and Micro F1 value of multi-classification of color fundus photographs by ResNet50 and ResNet50-OC were evaluated.Results:The multi-classification accuracy and Micro F1 values of color fundus photographs of ResNet50 were significantly higher than those of VGG, Inception-resnet-v2, ResNet34 and DenseNet.The accuracy of multi-classification of fundus photographs in the ResNet50-OC model was 98.77% after 15 rounds of training, which was higher than 98.76% of the ResNet50 model after 50 rounds of training.The Micro F1 value of multi-classification of retinal images in ResNet50-OC model was 98.78% after 15 rounds of training, which was the same as that of ResNet50 model after 50 rounds of training.Conclusions:The proposed ResNet50-OC model can be accurate and effective in the multi-classification of color fundus photograph quality.One-Cycle strategy can reduce the frequency of training and improve the classification efficiency.
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