Abstract
Diabetic retinopathy (DR) is a common ocular complication in diabetic patients and is a major cause of blindness in the population. DR often leads to progressive changes in the structure of the vascular system and causes abnormalities. In the process of DR analysis, the image quality needs to be evaluated first, and images with better imaging quality are selected, followed by value-added proliferative diabetic retinopathy (PDR) detection. Therefore, in this paper, the MixNet classification network was first used for image quality assessment (IQA), and then the ResNet50-CMBA network was used for DR grading of images, and both networks were combined with a k-fold cross-validation strategy. We evaluated our method at the 2022 Diabetic Retinopathy Analysis Challenge (DRAC), where image quality was evaluated on 1103 ultra-wide optical coherence tomography angiography (UW-OCTA) images and DR grading was detected on 997 UW-OCTA images. Our method achieved a Quadratic Weight Kappa of 0.7547 and 0.8010 in the test cases, respectively.
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Zhang, W., Chen, H., Li, D., Zheng, S. (2023). Automatic Image Quality Assessment and DR Grading Method Based on Convolutional Neural Network. In: Sheng, B., Aubreville, M. (eds) Mitosis Domain Generalization and Diabetic Retinopathy Analysis. MIDOG DRAC 2022 2022. Lecture Notes in Computer Science, vol 13597. Springer, Cham. https://doi.org/10.1007/978-3-031-33658-4_16
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DOI: https://doi.org/10.1007/978-3-031-33658-4_16
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