A Computer-Aided Feasibility Implementation to Detect Monkeypox from Digital Skin Images with Using Deep Artificial Intelligence Methods
Bibliographic record
Abstract
A sudden outbreak of Monkeypox disease has been reported recently in up to 70 countries so far and the spreading rate may be seen significantly around the world.The clinical aspects of Monkeypox have been reported that this disease looks like similar in many attributes when comparing some specific skin lesions as Chickenpox, Measles etc.These similarities make Monkeypox diagnosing and detecting difficult for doctors, clinicians or professionals by examining the visual appearance of the lesion on the skin.In addition, there has been a problem with the lack of detailed information about ultimate diagnosing of novel Monkeypox disease.It is also important that by the success of the studies about AI, Machine Learning and Deep Learning models in COVID-19 detection, the community has begun to give importance to detect Monkeypox via comprehensive AI methods from digital skin images.Moreover, in this paper, we develop a larger dataset to study and analyze the feasibility of common Artificial Intelligence based Deep Learning methods on skin images for Monkeypox detection.Our study has shown that Deep Learning models have a great and important success for detecting this disease from digital skin images via modifying/ updating some layers in the Transfer Learning.The other important information can be explained as because of being quite similar in some aspects to the other skin lesions and the lack of the detailed attributes/features of Monkeypox, detecting via specific AI models with Feature extraction process have become a bit difficult, unknown and time consuming in contrast to the Deep Learning models (AlexNet and VGG16 models in MATLAB software).The future aim is to develop a prototype web application and it is important that to improve the accuracy of Monkeypox detection, a larger demographically diverse dataset is required.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.015 | 0.003 |
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".