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This study introduces an advanced diagnostic framework leveraging hyperspectral imaging (HSI) to enhance SCC detection accuracy. The proposed methodology integrates Gray\u2010Level Co\u2010occurrence Matrix, Gabor filters, and Local Binary Patterns for spatial feature extraction, combined with Gramian Angular Field, Markov Transition Field, and Recurrence Plot for spatio\u2010spectral feature transformation. A novel multi\u2010scale hybrid transformer (MSHT) model is developed to classify skin lesions using microscopic HSI data, capturing both local texture details and global spectral\u2010spatial dependencies through hybrid convolutional and self\u2010attention mechanisms. Comparative experiments demonstrate the MSHT model's superior performance, achieving sensitivities of 0.88, 0.84, and 0.87 for actinic keratosis (AK), seborrheic keratosis (SK), and SCC, respectively. 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