Melanoma Classification Combining Browning Index and Deep Neural Networks
Bibliographic record
Abstract
Cutaneous melanoma, which originates from melanin producing cells (a substance that determines skin color), is the rarest and most dangerous type of skin cancer.Due to its high ability to spread to other areas of the body, early detection of cutaneous melanoma is the main action for reducing its mortality rates.By using computer-aided diagnosis that integrates some tools, such as image processing and deep learning, it is possible to develop efficient computational non-invasive approaches to cutaneous melanoma segmentation and recognition tasks.Unlike traditional approaches that directly process colorful original images, we propose to previously use a pre-processing step to highlight the skin melanoma from the rest of the skin before the segmentation process.The proposed solution consists of employing indicators of browning, called Browning Index (BI), used in some areas of the food industry.Three BIs (Aimonino, Lunadei2, and Fetuga) were applied separately and together in the images.The segmentation process was performed through two thresholding techniques (Otsu and Lloyd) and the morphological Watershed technique.The melanoma classification process, applied to previously segmented images, employed three CNN models: VGG19, ResNet50, and Xception.The results of the experiments show that previously highlighting the region of skin melanoma through the browning index aids in the classification process.The most promising results were obtained with the combination of the IDAT PWBHEPL, Lunadei2 Browning Index, Lloyd thresholding technique, and Xception CNN model, with an F1 score value of 97.05%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".