SkinCancerNet: Automated Classification of Skin Lesion Using Deep Transfer Learning Method
Bibliographic record
Abstract
Skin cancer has become one of the most common diseases due to the depletion of the ozone layer and the decrease in its protection.Detection and classification of skin cancer in the early stages of its development allows patients to receive appropriate treatment quickly.In this article, a modified CNN framework based on transfer learning is proposed for the classification of skin lesions from skin dermoscopy images.In the proposed framework, pretrained CNN architectures are used.VGG16, ResNet50, DeneNet121, MobileNet, and Xception models were pre-trained using ImageNet images and training weights.In the study training and tests were performed on the HAM10000 skin lesions data set.The classification accuracy of the modified DenseNet121, VGGNet16, ResNet50, MobileNet, and Xception models were calculated as 94.29%, 93.28%, 87.10%, 83.10%, and 80.05% respectively.It was observed that the accuracy success of the proposed transfer learning framework in skin lesion type classification surpasses classical deep learning architectures.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".