Graph-enhanced deep learning for diabetic retinopathy diagnosis: A quality-aware and uncertainty-driven approach

doi:10.1371/journal.pcbi.1013745

Graph-enhanced deep learning for diabetic retinopathy diagnosis: A quality-aware and uncertainty-driven approach

Table 5

Comparison with SOTA: After training the models using three different approaches—(1) applying CLAHE (), (2) applying Ben Graham’s preprocessing technique (♦), and (3) our proposed () pipeline without sophisticated preprocessing—we evaluated them on the actual test sets of the APTOS2019 and Messidor-2 datasets.

Our approach outperformed all existing benchmarks for DR classification on both datasets. Whether using CNN () or Transformer architectures (), our method consistently achieved superior performance compared to all previous DR classification methods.

doi: https://doi.org/10.1371/journal.pcbi.1013745.t005