Efficient Feature Based Melanoma Skin Image Classification Using Machine Learning Approaches
Bibliographic record
Abstract
Skin cancer is the most prevalent and deadliest kind of cancer. Melanoma is the most dangerous type of skin cancer, but it can be detected earlier and successfully treated. The dermoscopic image classification using Machine Learning (ML) approaches in identifying melanoma is increased over the last two decades. The proposed classification system involves three stages. Initially, the pre-processing employs the median filter and thresholding approach aids to remove the hairs and unwanted noise. Then, the shape components, Asymmetry, Border Irregularity, Colour and Dermoscopic structure (ABCD) rule, and Grey Level Co-occurrence Matrix (GLCM) features are utilized to extract the skin lesion region. After that, the K-Nearest Neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM) classifiers are employed to perform melanoma skin classification from the skin lesion. The dermoscopic skin images used in this study are obtained from the PH2 database. Finally, the SVM classifier outperformed the other classifiers in identifying melanoma skin cancer, providing 94.81% efficiency.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".