Improving Skin Lesion Classification with Attention Based Deep Learning Approach
Bibliographic record
Abstract
Skin cancer and other dermatological conditions pose significant health risks, necessitating an early and correct diagnosis for appropriate treatment. Traditional diagnosis approaches rely significantly on the dermatologist’s expertise, which may be time-consuming and subjective. This study presents a deep learning-based framework aimed at enhancing skin lesion classification using a Convolutional Neural Network with Convolutional Block Attention Module architecture (CNN-CBAM). The experimentation was performed on the HAM10000 dataset, which comprises diverse dermoscopic images of skin lesions. After extensive experimentation with model architecture and data augmentation techniques, the model provides an accuracy of 79% and an F1-score of $\mathbf{0. 7 5}$, showcasing strong performance in differentiating between various skin cancer types. The incorporation of CBAM enhanced the model’s feature extraction and localization abilities, leading to a notable improvement in classification accuracy. This method highlights the accurate skin cancer detection, supporting early diagnosis, and improving healthcare outcomes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 0.001 |
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".