Deep Learning-Based Diagnostic Framework for Early Detection of Jaundice Using Skin and Eye Image Analysis
Bibliographic record
Abstract
Jaundice, a yellowing of skin and eyes by elevated bilirubin, is an important early sign of liver disease. This paper suggests here a deep learning-based diagnosis system with inspection of images of skin and eyes to detect jaundice in its early stage. A 5,000 annotated image custom-made dataset is used with a specially created database in which the system combines convolutional neural networks (CNNs) for processing and extracting color features. The model developed was shown to have accuracy, sensitivity, and specificity of 94.6 %, 92.3 %, and 95.1 %, respectively, for jaundice detection. Diagnostic accuracy was greatly improved with a combination of skin and scleral image data. The computer-assisted, noninvasive technique promises large-scale screening and telemedicine-based medical treatment, enabling early medical treatment and clinical workload relief.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".