# Integrating Non-Linear Radon Transformation for Diabetic Retinopathy Grading

arXiv: https://arxiv.org/abs/2504.15883 · HTML: https://mlsanity.com/paper/2504.15883/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 42 references that are in the map.
- First posted on arXiv 22 Apr 2025.

## Builds on (most cited first, 11 of 11)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Vision GNN: An Image is Worth Graph of Nodes](https://mlsanity.com/paper/2206.00272/) (2022)
- [CANet: Cross-disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading](https://mlsanity.com/paper/1911.01376/) (2019)
- [Artificial Intelligence-Based Methods for Fusion of Electronic Health Records and Imaging Data](https://mlsanity.com/paper/2210.13462/) (2022)
- [Blended Multi-Modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction](https://mlsanity.com/paper/2006.00197/) (2020)
- [GREEN: a Graph REsidual rE-ranking Network for Grading Diabetic Retinopathy](https://mlsanity.com/paper/2007.09968/) (2020)
- [Distractor-Aware Neuron Intrinsic Learning for Generic 2D Medical Image Classifications](https://mlsanity.com/paper/2007.09979/) (2020)
- [Dual Branch Deep Learning Network for Detection and Stage Grading of Diabetic Retinopathy](https://mlsanity.com/paper/2308.09945/) (2023)

## Built on by (most cited first, 2 of 2)

- [Multi-Channel Feature Fusion and Monte Carlo Dropout for Uncertainty-Aware Diabetic Retinopathy Grading](https://mlsanity.com/paper/2608.15234/) (2026)
- [HierarchiRetina: A Diabetic Retinopathy Screening and Grading Cascade with Heterogeneous Mixture-of-Experts Lesion Segmentation](https://mlsanity.com/paper/2610.17933/) (2026)

## Abstract (arXiv)

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 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 capture complex retinal lesion patterns. By leveraging both spatial and transformed domain information, RadFuse enriches the feature set available to deep learning models, improving the differentiation of severity levels. We conducted extensive experiments on two benchmark datasets, APTOS-2019 and DDR, using three convolutional neural networks (CNNs): ResNeXt-50, MobileNetV2, and VGG19. RadFuse showed significant improvements over fundus-image-only models across all three CNN architectures and outperformed state-of-the-art methods on both datasets. For severity grading across five stages, RadFuse achieved a quadratic weighted kappa of 93.24%, an accuracy of 87.07%, and an F1-score of 87.17%. In binary classification between healthy and diabetic retinopathy cases, the method reached an accuracy of 99.09%, precision of 98.58%, and recall of 99.6%, surpassing previously established models. These results demonstrate RadFuse's capacity to capture complex non-linear features, advancing diabetic retinopathy classification and promoting the integration of advanced mathematical transforms in medical image analysis.