Diagnosis of Melanoma Using Differential Evolution Optimized Artificial Neural Network
Bibliographic record
Abstract
Melanoma, a prevalent invasive skin cancer, is treatable in its early stages with timely diagnosis and prompt intervention.However, accurately diagnosing lesions through visual inspection or based on their characteristics remains challenging for physicians.Medical imaging techniques play a crucial role in the rapid and precise prognosis of skin lesions.Our research focuses on analyzing and classifying early-stage melanoma using machine learning techniques.In this paper, we propose a metaheuristic algorithm, Differential Evolution optimized Artificial Neural Network (DEO-ANN), for melanoma diagnosis.Color features are assessed from the Region of Interest (ROI) of the lesion using RGB and opponent color space to enhance classification accuracy.Classification is performed using an artificial neural network trained by a differential evolution algorithm.Simulated output demonstrates that the trained DEO-ANN classifier achieves an Area Under Curve (AUC) of 0.98966 with an accuracy of 94.9% on an ISIC dataset.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".