Skin cancer is a highly dangerous type of cancer that requires an accurate diagnosis from experienced physicians. To help physicians diagnose skin cancer more efficiently, a computer-aided diagnosis (CAD) system can be very helpful. In this paper, we propose a novel model, which uses a novel attention mechanism to pinpoint the differences in features across the spatial dimensions and symmetry of the lesion, thereby focusing on the dissimilarities of various classes based on symmetry, uniformity in texture and color, etc. Additionally, to take into account the variations in the boundaries of the lesions for different classes, we employ a gradient-based fusion of wavelet and soft attention-aided features to extract boundary information of skin lesions. We have tested our model on the multi-class and highly class-imbalanced dataset, called HAM10000, and achieved promising results, with a 91.17\% F1-score and 90.75\% accuracy. The code is made available at: https://github.com/AyushRoy2001/WAGF-Fusion.

通过使用新颖的注意机制来聚焦于不同对称性、质地和颜色等特征的差异以及提取皮损边界信息的梯度融合小波和软注意辅助特征，我们的模型在多类别且高度不平衡的数据集HAM10000上测试并取得了有希望的结果，F1得分为91.17%，准确率为90.75%。

基于小波引导的注意力模块与基于梯度的特征融合的皮肤癌分类