Experimental Science
Evaluation of multi-classification method of color fundus photograph quality based on ResNet50-OC
Wan Cheng, Zhou Xueting, You Qijing, Shen Jianxin, Yu Qiuli
Published 2021-09-10
Cite as Chin J Exp Ophthalmol, 2021, 39(9): 785-790. DOI: 10.3760/cma.j.cn115989-20200107-00011
Abstract
ObjectiveTo evaluate the efficiency of ResNet50-OC model based on deep learning for multiple classification of color fundus photographs.
MethodsThe 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.
ResultsThe 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.
ConclusionsThe 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.
Key words:
Artificial intelligence; Image quality classification; Convolutional neural network; One-Cycle learning; Color fundus photograph
Contributor Information
Wan Cheng
College of Electronic Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Zhou Xueting
College of Electronic Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
You Qijing
College of Electronic Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Shen Jianxin
College of Electronic Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Yu Qiuli
Department of Ophthalmology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing 210003, China
