Analysis of the Impact of Color Spaces on Skin Cancer Diagnosis Using Deep Learning Techniques
Bibliographic record
Abstract
Skin cancer diagnosis forms a critical aspect of medical research, with notable improvements being driven by artificial intelligence (AI), particularly deep learning. This study is focused on a specific, crucial challenge: enhancing the diagnostic accuracy of skin cancer by leveraging the color information inherent in skin lesions. To meet this aim, an innovative method combining convolutional neural networks and deep learning-based image processing techniques was developed. The proposed methodology exploits various color spaces, including RGB, Lab, HSV, and YUV, to meticulously analyze skin lesion color characteristics. A comprehensive exploration of numerous color space combinations revealed the superior performance of the YUV-RGB blend. An impressive accuracy of 98.51% was attained in the detection and classification of different types of skin cancer using this combination, surpassing conventional diagnostic approaches in both speed and precision. These significant findings pave the way for early skin cancer detection, dramatically enhancing treatment possibilities and patient recovery prospects. This study, therefore, provides a substantial contribution to the domain of skin cancer diagnosis by fully harnessing the potential of AI and deep learning.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".