Examining the Performance of Melanoma Classification using Superpixel Segmentation: A Comparative Analysis
Bibliographic record
Abstract
Skin cancer, characterized by the abnormal growth of skin cells, is a severe and prevalent condition. Despite the advancement in digital diagnosis techniques, existing skin cancer detection methods often fail to achieve satisfactory performance in melanoma identification in dermoscopy imaging. This study presents a new melanoma classification algorithm that utilizes a superpixel-based segmentation technique, simple linear iterative clustering, and transfer learning-based convolutional neural networks to achieve the objective. A key innovation of our methodology is the introduction of a novel approach for efficiently merging superpixels, that enhances the quality of segmentation resulting in better classification performance. Following the segmentation phase, several convolutional neural networks have been utilized for feature extraction from segmented images and classification. The proposed method shows an enhanced performance in melanoma classification which is 91.67%, 95.33%, 91.23%, and 90.48% in terms of accuracy, precision, recall and F1-score, respectively. Our results indicate that the superpixel segmentation technique considerably enhances the classification models’ accuracy compared to k-means segmentation methods. A comparative analysis between the proposed method and several state-of-the-art methods in the field has also been presented in the context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.001 | 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".