Hyperspectral imaging has emerged as a promising technology for medical image classification, particularly in skin cancer diagnosis. However, current methods face significant challenges in accurately and robustly classifying non-cancerous skin lesions, especially when melanoma lesions overlap with pigmented regions. Existing methods also lack sensitivity to spectral variations and accumulate excess redundant data, leading to inefficiencies, misclassifications, and overfitting while struggling to integrate spatial and spectral information effectively. To overcome these chal-lenges, we propose a novel method featuring collaborative sparse unmixing and an advanced pixel-wise shuffling approach with inter-similarity hybrid attention, aiming to improve the accuracy of skin cancer diagnosis in real-world scenarios. Experiments are conducted on a publicly available histology-verified dataset to evaluate the efficacy of the proposed method. The experimental results demonstrate that the proposed method can accurately classify melanoma lesions, even in cases where the lesions overlap with pig-mented regions. The findings indicate that the proposed method outperforms state-of-the-art methods by obtaining an overall accuracy of 73.34%, even when limited to 20% of the training data. The proposed approach has the potential to be a valuable tool for improving the diagnostic accuracy of skin cancer in clinical practice.