Integrating Non-Linear Radon Transformation for Diabetic Retinopathy Grading

• URL: http://arxiv.org/abs/2504.15883v1
• Date: Tue, 22 Apr 2025 13:27:28 GMT
• Title: Integrating Non-Linear Radon Transformation for Diabetic Retinopathy Grading
• Authors: Farida Mohsen, Samir Belhaouari, Zubair Shah,
• Abstract summary: Diabetic retinopathy is a serious ocular complication that poses a significant threat to patients' vision and overall health.<n>Current automatic grading methods rely heavily on deep learning applied to retinal fundus images.<n>This study introduces RadFuse, a multi-representation deep learning framework that integrates non-linear RadEx-transformed sinogram images with traditional fundus images.
• Score: 0.08192907805418582
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

Related papers

• VR-FuseNet: A Fusion of Heterogeneous Fundus Data and Explainable Deep Network for Diabetic Retinopathy Classification [0.0]
  This paper presents a comprehensive approach for automated diabetic retinopathy detection by proposing a new hybrid deep learning model called VR-FuseNet. The proposed VR-FuseNet model combines the strengths of two state-of-the-art convolutional neural networks, VGG19 which captures fine-grained spatial features and ResNet50V2 which is known for its deep hierarchical feature extraction. The model outperforms individual architectures on all performance metrics demonstrating the effectiveness of hybrid feature extraction in Diabetic Retinopathy classification tasks.
  arXiv  Detail & Related papers  (2025-04-30T09:38:47Z)
• Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations [40.8160960729546]
  Interpretability is crucial to enhance trust in machine learning models for medical diagnostics. This work proposes a method that surpasses the performance of established machine learning models.
  arXiv  Detail & Related papers  (2025-02-23T19:27:47Z)
• A Value Mapping Virtual Staining Framework for Large-scale Histological Imaging [36.95712533471744]
  We introduce a general virtual staining framework that is adaptable to various conditions. We propose a loss function based on the value mapping constraint to ensure the accuracy of virtual coloring between different pathological modalities.
  arXiv  Detail & Related papers  (2025-01-07T07:45:21Z)
• Enhancing Diabetic Retinopathy Detection with CNN-Based Models: A Comparative Study of UNET and Stacked UNET Architectures [0.0]
  Diabetic Retinopathy DR is a severe complication of diabetes. Damaged or abnormal blood vessels can cause loss of vision.<n>The need for massive screening of a large population of diabetic patients has generated an interest in a computer-aided fully automatic diagnosis of DR.<n>Deep learning frameworks, particularly convolutional neural networks CNNs, have shown great interest and promise in detecting DR by analyzing retinal images.
  arXiv  Detail & Related papers  (2024-11-02T14:02:45Z)
• Controllable retinal image synthesis using conditional StyleGAN and latent space manipulation for improved diagnosis and grading of diabetic retinopathy [0.0]
  This paper proposes a framework for controllably generating high-fidelity and diverse DR fundus images. We achieve comprehensive control over DR severity and visual features within generated images. We manipulate the DR images generated conditionally on grades, further enhancing the dataset diversity.
  arXiv  Detail & Related papers  (2024-09-11T17:08:28Z)
• Analysis of the Two-Step Heterogeneous Transfer Learning for Laryngeal Blood Vessel Classification: Issue and Improvement [8.7937485450551]
  This work pioneers exploring two-step heterogeneous transfer learning (THTL) for laryngeal lesion classification. We use diabetic retinopathy color fundus images, semantically non-identical yet vascular images, as the intermediate domain. We introduce an enhanced fine-tuning strategy in THTL called Step-Wise Fine-Tuning (SWFT) and apply it to the ResNet models.
  arXiv  Detail & Related papers  (2024-02-29T09:52:39Z)
• Performance of GAN-based augmentation for deep learning COVID-19 image classification [57.1795052451257]
  The biggest challenge in the application of deep learning to the medical domain is the availability of training data. Data augmentation is a typical methodology used in machine learning when confronted with a limited data set. In this work, a StyleGAN2-ADA model of Generative Adversarial Networks is trained on the limited COVID-19 chest X-ray image set.
  arXiv  Detail & Related papers  (2023-04-18T15:39:58Z)
• DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images [51.27125547308154]
  We organized a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022) The challenge consists of three tasks: segmentation of DR lesions, image quality assessment and DR grading. This paper presents a summary and analysis of the top-performing solutions and results for each task of the challenge.
  arXiv  Detail & Related papers  (2023-04-05T12:04:55Z)
• An Ensemble Method to Automatically Grade Diabetic Retinopathy with Optical Coherence Tomography Angiography Images [4.640835690336653]
  We propose an ensemble method to automatically grade Diabetic retinopathy (DR) images available from Diabetic Retinopathy Analysis Challenge (DRAC) 2022. First, we adopt the state-of-the-art classification networks, and train them to grade UW- OCTA images with different splits of the available dataset. Ultimately, we obtain 25 models, of which, the top 16 models are selected and ensembled to generate the final predictions.
  arXiv  Detail & Related papers  (2022-12-12T22:06:47Z)
• Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation [48.638327652506284]
  Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms. We present a novel approach, the affinity feature strengthening network (AFN), which jointly models geometry and refines pixel-wise segmentation features using a contrast-insensitive, multiscale affinity approach.
  arXiv  Detail & Related papers  (2022-11-12T05:39:17Z)
• A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability [76.64661091980531]
  People with diabetes are at risk of developing diabetic retinopathy (DR) Computer-aided DR diagnosis is a promising tool for early detection of DR and severity grading. This dataset has 1,842 images with pixel-level DR-related lesion annotations, and 1,000 images with image-level labels graded by six board-certified ophthalmologists.
  arXiv  Detail & Related papers  (2020-08-22T07:48:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.