Accelerated Deep Learning Framework With Hybrid Techniques for Hair Disease Diagnosis and Telemedicine Integration
Bibliographic record
Abstract
Background information: Alopecia and seborrheic dermatitis are two dermatoses that lower quality of life. The current diagnoses are subjective and time-consuming. EfficientNet-B3 and CBAM, two deep learning-based telemedicine tools, offer scalable, accurate solutions to the issues of data scarcity and class imbalance. Objectives: The hybrid deep learning system, by combining EfficientNet-B3, CBAM, and GANs, will address class imbalance and provide a simpler platform for telemedicine-based accurate, rapid, and scalable hair illness diagnosis. Methods: Harvesting of features is performed by using EfficientNet-B3 and CBAM. GANs are applied for rectification of the synthetic data class imbalance problem. For the purpose of achieving light scaling, knowledge distillation is used, while preprocessing, augmentation, and RAdam are utilized for the optimization of training. Results: With a delay of 15 ms, 99.5% accuracy and 98.9% precision were achieved. Conclusion: In conclusion, it helps underprivileged communities and ensures real-time scalability with accuracy in diagnosis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".