Deep Learning-Based Diagnostic Model for Automated Detection of Monkeypox: Introducing MonkeypoxNet
Bibliographic record
Abstract
The swift and accurate detection of the Monkeypox virus (MPV) is integral to effective patient management and disease control.Current artificial intelligence (AI) methodologies in computer-aided diagnosis (CAD) have shown limitations in their predictive performance for MPV.Addressing this, we have developed a novel deep learning (DL) network, named MonkeypoxNet, that excels in the detection of MPV from skin images.Initially, a dataset comprising skin images, differentiated into normal and MPV-specific classes, was assembled.Image pre-processing was then conducted employing contrast-limited adaptive histogram equalization (CLAHE), which enhanced the colour and texture attributes of the images.Subsequently, a novel integration of genetic algorithm with particle swarm optimization (GA-PSO) was devised to extract and select the most relevant features from the pre-processed dataset.These features were then utilized to train our custom deep convolutional neural network (CDCNN) model.The CDCNN model was subsequently used for prediction, distinguishing between MPV and normal disease classes by comparing test features with the trained model.Remarkably, the proposed MonkeypoxNet demonstrated an accuracy of 99.06%, sensitivity of 98.66%, specificity of 99.11%, and an F-measure of 99.67%.Comparative analysis with existing methodologies confirmed that our proposed approach outperforms in all evaluated metrics.The successful implementation of MonkeypoxNet, underscored by its exceptional accuracy and efficiency, holds the potential to revolutionize early detection and diagnosis of monkeypox virus infections.This could ultimately lead to improved patient outcomes and facilitate timely interventions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".