DR-ConvNeXt: DR classification method for reconstructing ConvNeXt model structure.

Abstract

Background: Diabetic retinopathy (DR) is a major complication of diabetes and a leading cause of blindness among the working-age population. However, the complex distribution and variability of lesion characteristics within the dataset present significant challenges for achieving high-precision classification of DR images.

Objective: We propose an automatic classification method for DR images, named DR-ConvNeXt, which aims to achieve accurate diagnosis of lesion types.

Methods: The method involves designing a dual-branch addition convolution structure and appropriately increasing the number of stacked ConvNeXt Block convolution layers. Additionally, a unique primary-auxiliary loss function is introduced, contributing to a significant enhancement in DR classification accuracy within the DR-ConvNeXt model.

Results: The model achieved an accuracy of 91.8%,sensitivity of 81.6%, and specificity of 97.9% on the APTOS dataset. On the Messidor-2 dataset, the model achieved an accuracy of 83.6%, sensitivity of 74.0%, and specificity of 94.6%.

Conclusions: The DR-ConvNeXt model's classification results on the two publicly available datasets illustrate the significant advantages in all evaluation indexes for DR classification.