Deep Learning-Based Classification of Melanoma and Non-Melanoma Skin Cancer
Bibliographic record
Abstract
Melanoma skin cancer is primarily characterized by poor prognostic responses.Surgical treatment can achieve advanced cure rate with early melanoma detection.Manual segmentation of suspected lesions aids early melanoma diagnosis.However, the limitations of manual segmentation include low efficiency and a risk of misclassification.Deep learning, due to its proficiency in image object classification, has gained popularity and is usually used in medical specialties such as ophthalmology, dermatology, and radiology.This paper proposes a deep learning method using a novel light weight convolutional neural networks (LWCNN) and transfer learning techniques (GoogleNet,.These are used to train datasets and features enhancement of skin scan gathered from Kaggle, aiming to distinguish them into two groups: Melanoma and Non-Melanoma cells.By employing these techniques, new datasets with robust features are produced.All CNN models have been tested in two experiments.In firestone, model was tested solely with original datasets and achieved 97.30%, 88.43%, and 48.28% for AC-Training, AC-Testing, and Time (min) respectively.In second experiment, we used the dataset after enhancing the features of skin scan images, which resulted in 99.18%, 91.05%, and 22.54% for AC-Training, AC-Testing, and Time (min) respectively.According to experimental results, the proposed approach provides higher accuracy results for enhanced images than original images, demonstrating its potential in skin cancer classification.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".