Integrating non-linear radon transformation for diabetic retinopathy grading.

ABSTRACT: Diabetic retinopathy is a serious ocular complication that poses a significant threat to patients' vision and overall health. Early detection and accurate grading are essential to prevent vision loss. Current automatic grading methods rely heavily on deep learning applied to retinal fundus images, but the complex, irregular patterns of lesions in these images, which vary in shape and distribution, make it difficult to capture the subtle changes. This study introduces RadFuse, a multi-representation deep learning framework that integrates non-linear RadEx-transformed sinogram images with traditional fundus images to enhance diabetic retinopathy detection and grading. Our RadEx transformation, an optimized non-linear extension of the Radon transform, generates sinogram representations to cap

SUBMITTER: Mohsen F 

PROVIDER: S-EPMC12370889 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature