Optical coherence tomography image recognition of diabetic retinopathy based on deep transfer learning

Objective

Diabetic retinopathy (DR) poses a significant challenge as a leading cause of vision impairment among diabetic individuals. Previous endeavors in optical coherence tomography (OCT) image segmentation using conventional deep learning methodologies have exhibited limitations in achieving robust generalization. Our study endeavors to explore the application of deep transfer learning models on OCT images for DR identification, juxtaposing their performance against conventional deep learning approaches.

Methods

Our investigation involved a cohort of 103 DR patients admitted to the ophthalmology department of our institution spanning from January 2023 to January 2024. Through a randomized allocation process, these patients were partitioned into distinct training and validation sets at a ratio of 7:3. Two convolution models, VGG19 and DenseNet, were constructed and transfer learning was carried out. The recognition effect of the traditional model and transfer model is compared and verified.

Results

Our findings demonstrate that both the VGG19 and DenseNet prediction models exhibit notable segmentation performance following transfer learning compared to their non-transfer learning counterparts. Post-transfer learning, the VGG model achieved accuracy, precision, recall, and F1-score values of 0.890, 0.924, 0.950, and 0.867, respectively, while the DenseNet model achieved corresponding values of 0.897, 0.900, 0.931, and 0.859. Furthermore, in the test set, the area under the curve (AUC) improved significantly for both models post-transfer learning, with the VGG model showcasing an AUC of 0.9118 and the DenseNet model exhibiting an AUC of 0.951.

Conclusion

The neural network model leveraging deep transfer learning demonstrates a notable enhancement in the recognition capability of DR based on OCT images. Furthermore, it effectively streamlines the workflow of ophthalmologists, thus warranting further promotion and adoption in clinical practice.