Bibliographic record
Abstract
This study addresses the critical challenge of accurately identifying skin disorders as benign, malignant, or non-tumors, essential for timely and successful treatment.Early identification can greatly minimize tumor development and cut fatality rates.Given the high costs involved with standard medical detection approaches, this research addresses using sophisticated Convolutional Neural Networks (CNNs) with transfer learning to categorize skin malignancies efficiently.Specifically, the study assesses the performance of MobileNetV2, VGG16, and VGG19 architectures.The primary objective is to find which model has the maximum accuracy in classifying skin cancers.Our findings reveal that while a standard CNN reached an accuracy of 62.2%, the transfer learning models greatly outperformed it, with MobileNetV2 achieving the highest accuracy at 93.9%, followed by VGG19 at 90.0% and VGG16 at 88.9%.These results imply that MobileNetV2 is the most successful solution for this task since it consistently obtained a prediction accuracy of 90% for both in-dataset and out-ofdataset images.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".