VirLesDetNet: Pre-Trained Hybrid Deep Learning Approaches for Virus-Based Skin Lesion Detection
Bibliographic record
Abstract
The monkeypox virus is a DNA virus with a double-stranded structure and belongs to the Orthopoxvirus family.While skin lesions are a major indicator of monkeypox, they are often indistinguishable from early-stage chickenpox and measles lesions, leading to potential misdiagnoses.To address this issue, a new hybrid deep learning model has been developed to classify skin lesions into four categories: normal, monkeypox, chickenpox, and measles, using the publicly available Monkeypox Skin Images Dataset (MSID).The dataset was initially expanded through image preprocessing and data augmentation techniques.Seven pre-trained deep learning models were then trained individually.After evaluating their performance, the top three models were selected, and an ensemble model was created to improve overall accuracy through majority voting based on the probabilistic outputs from these models.The model's effectiveness is validated by accuracy, recall, precision, F1 score, and a confusion matrix.The proposed ensemble model, which combines EfficientB3, ResNet152, and MobileNetV3, achieved a detection accuracy rate of 94.82%.
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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) | 0.000 | 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) | 0.001 | 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".