Advanced hybrid UNet architectures for retinal vessel segmentation
Bibliographic record
Abstract
Retinal blood vessel segmentation is a critical step in the early detection and diagnosis of various vision-threatening diseases, including diabetic retinopathy, hypertension, and glaucoma. Manual segmentation by medical professionals is time-consuming, subjective, and prone to variability, highlighting the need for robust automated solutions. Recent advancements in deep learning have shown significant promise in addressing these challenges by enabling accurate and efficient segmentation of retinal blood vessels from fundus images. In this paper, we propose three advanced deep learning architectures for retinal blood vessel segmentation: VGG16-UNet, VGG19-UNet, and ResNet50-UNet. These models combine the strengths of pre-trained convolutional neural networks (CNNs) with the U-Net architecture, leveraging transfer learning to enhance feature extraction and segmentation performance. We evaluate the proposed models on a publicly available retinal image dataset, achieving Dice coefficients of 79%, 79% and 80% for VGG16-UNet, VGG19-UNet, and ResNet50-UNet, respectively. Our results demonstrate that the ResNet50-UNet model outperforms the other two variants and surpasses several state-of-the-art methods in terms of segmentation accuracy and robustness. This study underscores the potential of deep learning-based approaches for improving the automation and reliability of retinal blood vessel segmentation, paving the way for more efficient and accurate diagnostic tools in ophthalmology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".