An Efficient End-to-end Convolutional Neural Network for Classification of Diabetic Retinopathy using ResNet
Bibliographic record
Abstract
Diabetes is a chronic disease affecting millions of people worldwide, and more than 25% of them have diabetic retinopathy (DR). DR is the leading cause of blindness in adults. DR is usually diagnosed with a biomicroscopic examination of the fundus after pupillary dilation, supplemented by fundus photographs. The challenge lies in the interpretation of these images by a specialist physician. In this work we propose a computer-assisted pipeline for the diagnosis of DR using the convolutional neural network (CNN). This pipeline has three main stages: (i) image preprocessing, (ii) feature extraction and (iii) classification. First, we used the Discrete Wavelet Transform (DWT) method for edge segmentation, and the Contrast Limited Adaptive Histogram Equalization (CLAHE) method to improve image contrast. In the second and third step we used the ResNet50 convolutional neural network for the detection and classification of five levels of disease severity (0 - No DR, 1 - Mild, 2 - Moderate, 3 - Severe, 4 - Proliferative) on 35 122 images from the publicly available Kaggle database.To analyze the performance of the learning model, different metrics were used to detect different diseases, such as sensitivity, specificity, and the confusion matrix which includes the number of true positive, false positive, true negative and false negative.In conclusion, we achieved 85% accuracy in the validation phase and 81% in the testing phase, demonstrating the reliability of CNNs to identify and automate the diagnosis of diabetic retinopathy using digital fundus images.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".