<b>Deep Learning-Based Monkeypox Detection: A</b> <b>Hybrid Approach Using DenseNet121 and</b> <b>MobileNetV2</b><b> </b>
Bibliographic record
Abstract
Due to recent outbreaks outside of endemic areas, Monkeypox is a newly emerging zoonotic disease that has drawn international attention. This study utilized two publicly available collections—the Monkeypox Skin Lesion Dataset (MSLD) and its updated version, MSLDV2.0, which consist of 2607 and 10572 clinical images, respectively. Clinical photographic images of confirmed Monkeypox lesions and comparative non-Monkeypox dermatoses. Early and precise lesion detection is therefore crucial for effective containment and treatment. We propose a hybrid deep-learning approach that fuses the hierarchical feature extraction capabilities of DenseNet121 with the computational efficiency of MobileNetV2 for reliable Monkeypox identification. On MSLDV2.0, our model achieved 98 % accuracy, and on MSLD, it reached 99.18 %. The confusion matrices confirm robust discrimination between Monkeypox and non-Monkeypox classes, outperforming existing methods. A key limitation of this work is the moderate size and demographic homogeneity of the datasets, which may not fully capture real-world variations in skin tone or lesion presentation. Future research should incorporate larger, multi-center image repositories, evaluate performance across diverse populations, and assess real-time deployment in resource-constrained clinical settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".