SkinAI – A Deep Learning Based Web Application for Multi-Class Skin Cancer Detection
Bibliographic record
Abstract
Skin cancer is one of the most prevalent cancers worldwide, and early detection significantly improves the chance of a favorable outcome. However, timely and affordable diagnostic help is often unavailable in disadvantaged communities. To close this gap, we present SkinAI, a web-based deep learning b system that classifies dermoscopic skin lesion photographs into seven diagnostic categories using the Xception convolutional neural network. Strong pre-processing methods like image augmentation and SMOTE are used to train the model on the HAM10000 dataset in order to address class imbalance. The system provides users with downloadable PDF reports, real-time predictions, and an easy-to-use interface that was created with Flask with TailwindCSS. SkinAI is an easy-to-use diagnostic tool that helps users make educated healthcare decisions by offering downloadable reports and real-time predictions, particularly in areas with limited access to dermatological care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".