Combining Anisotropic Heat Transfer and Transformer Encoding within Convolutional Neural Networks for the Classification of Skin Cancer

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The study introduces an Advanced Heat Flow Layer integrated with DenseNet121 for improved skin cancer classification.
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Anisotropic diffusion is utilized to enhance image quality while preserving critical edge details for accurate lesion identification.
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The model is evaluated using the HAM10000 dataset, which contains over 10,000 diverse dermatoscopic images.
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Ensemble Learning is employed to combine outputs from multiple models, enhancing predictive accuracy in skin cancer classification.
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The research compares the proposed model against benchmark deep learning models, demonstrating superior performance across key metrics.

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