Predictive Analytics for Tongue Disease Diagnosis: A Comparative Study of Deep Learning Models
Bibliographic record
Abstract
Tongue disease can be the forerunner of health disorder of the system, and correct diagnosis must thus be performed in order to do something at the appropriate time. Predictive analytics are employed within this study for comparison of deep learning architectures - Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and transfer learning architectures viz. VGG16, ResNet50, and EfficientNetB0 - for tongue disease diagnosis. With a dataset of 12,000 tongue images collected over 8 disease classes, models were evaluated on accuracy, precision, recall, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{F}1$</tex>-score. ResNet50 recorded 94.7 % accuracy over CNN (87.3 %) and RNN (82.1 %). EfficientNetB0 also recorded a 28% inference speedup with near zero performance loss cost. The article illustrates that the power of transfer learning may be utilized for making non-invasive diagnostics less sensitive in order to drive their clinical uptake at a faster speed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".