Abstract
Purpose
Diabetic retinopathy is one of the main complications of diabetes mellitus. To assess the progression of the disease, retinal images are commonly obtained through fundus examination. However, those images may present problems such as low contrast, inadequate lighting, and unsatisfactory noise level, among other aspects that can compromise medical analysis and intervention. In this context, this work aims to apply the neural network VGG16 to classify the diabetic retinopathy in 5 categories and with an additional class (named class 5) to report low quality of digital retinal images publicly available in the DDR, EyePACS/Kaggle, and IDRiD databases.
Methods
The proposed methodology includes pre-processing of retinal images, consisting of size adequacy, data cleaning (the removal of low-quality images from other classes and inclusion of them in class 5), data augmentation and class balancing during the training phase, and hyperparameter adjustment and image classification using the VGG16 neural network.
Results
Among the tests carried out on the three databases, this proposal has been presented the best performance for DDR database regarding accuracy, precision, sensitivity, specificity, and F1-score.
Conclusion
This work contributes towards the improvement of results achieved in the state-of-the-art for DDR and IDRiD databases without DME, and the inclusion of performance metrics sensitivity, specificity, precision, and F1-score.
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Funding
This study was financed in part by CAPES—Coordenação de Aperfeiçoamento de Pessoal de Nível Superior — Brasil — Finance Code 001.
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da Rocha, D.A., Ferreira, F.M.F. & Peixoto, Z.M.A. Diabetic retinopathy classification using VGG16 neural network. Res. Biomed. Eng. 38, 761–772 (2022). https://doi.org/10.1007/s42600-022-00200-8
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DOI: https://doi.org/10.1007/s42600-022-00200-8