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9 January 2024 Detection algorithm for diabetic retinopathy based on ResNet and transfer learning
Weihua Wang, Li Lei
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Proceedings Volume 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023); 129690G (2024) https://doi.org/10.1117/12.3014400
Event: International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023), 2023, Qingdao, China
Abstract
DR (Diabetic retinopathy) a chronic progressive disease which affects eyesight and even causes blindness. It is significance to carry out the identification and severity diagnosis of DR, timely diagnosis and treatment of DR Patients, improve the people’s quality, especially the elderly, and improve the efficiency of diagnosis. In this study, with the goal of efficient and accurate division of DR Levels, a DR Recognition and classification algorithm based on ResNet and transfer learning is proposed. Firstly, shallow feature extraction module of ResNet18 is used to get retinal image feature, and then the fully connected classification structure model of DR Is designed. Then the transfer learning method is combined to train the network weights to improve the generalization ability of the model, ResNet-18 is selected as the backbone network model for feature extracting. Results show that the accuracy of the training set reaches to provide useful guidance for DR Automatic diagnosis, and effectively alleviates the problem of low accuracy of DR Classification
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Weihua Wang and Li Lei "Detection algorithm for diabetic retinopathy based on ResNet and transfer learning", Proc. SPIE 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 129690G (9 January 2024); https://doi.org/10.1117/12.3014400
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KEYWORDS
Education and training
Machine learning
Data modeling
Feature extraction
Image classification
Image processing
RGB color model
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