机构：[1]Department of Biomedical Engineering, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing University of Technology, Beijing 100124, China[2]Beijing Tongren Eye Center, Beijing Ophthalmology & Visual Sciences Key Lab, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China首都医科大学附属北京同仁医院首都医科大学附属同仁医院[3]Sports and Medicine Integrative Innovation Center, Capital University of Physical Education and Sports, Beijing 100191, China
Diabetic retinopathy (DR) and diabetic macular edema (DME) are the major causes of permanent blindness in the working-age population. Deep learning methods have been proposed to automatically grade DR and DME for ophthalmologists' design of tailored treatments for patients. However, these methods are computationally intensive with a large number of parameters and affect the optimization of hyperparameters, making them challenging to deploy to mobile or embedded devices with limited computer resources. In this paper, we developed a transfer learning-based lightweight convolutional neural network to jointly classify the severity of DR and DME. Using fivefold cross-validation, our model achieved an average accuracy, precision, recall, specificity, and F1-score of 0.9666, 0.9700, 0.9685, 0.9932, and 0.9663, respectively, better than MobileNet V2, while the number of parameters and the recognition speed were dramatically less than those of MobileNet V2 and ResNet50. These results show that our model is hopeful in diagnosing retinopathy in clinical trials, even when configured for mobile and embedded devices.
基金：
The authors gratefully acknowledge the financial support of National Natural Science Foundation of China and the Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological.
第一作者机构：[1]Department of Biomedical Engineering, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing University of Technology, Beijing 100124, China
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推荐引用方式(GB/T 7714)：
Lu Zhenzhen,Miao Jingpeng,Dong Jingran,et al.Automatic classification of retinal diseases with transfer learning-based lightweight convolutional neural network[J].BIOMEDICAL SIGNAL PROCESSING AND CONTROL.2023,81:doi:10.1016/j.bspc.2022.104365.
APA：
Lu, Zhenzhen,Miao, Jingpeng,Dong, Jingran,Zhu, Shuyuan,Wang, Xiaobing&Feng, Jihong.(2023).Automatic classification of retinal diseases with transfer learning-based lightweight convolutional neural network.BIOMEDICAL SIGNAL PROCESSING AND CONTROL,81,
MLA：
Lu, Zhenzhen,et al."Automatic classification of retinal diseases with transfer learning-based lightweight convolutional neural network".BIOMEDICAL SIGNAL PROCESSING AND CONTROL 81.(2023)
