Objective:

To integrate an Advanced Heat Flow Layer and transformer encoding within deep learning architectures for skin cancer classification.

Key Findings:

• The proposed model outperformed benchmark models across key metrics including accuracy, precision, recall, F1 score, and AUC.
• The Advanced Heat Flow Layer effectively preserved critical edge details while smoothing images.
• Ensemble Learning enhanced the robustness and reliability of skin cancer classification.

Interpretation:

Limitations:

• The study does not claim novelty in anisotropic diffusion or CNN-Transformer architectures individually.
• The effectiveness of the ensemble approach may vary depending on the diversity of the models used.

Conclusion:

The study presents advanced image processing techniques within a deep learning framework for skin cancer classification.