  Skin cancer holds the highest incidence rate among all cancers globally. The
importance of early detection cannot be overstated, as late-stage cases can be
lethal. Classifying skin lesions, however, presents several challenges due to
the many variations they can exhibit, such as differences in colour, shape, and
size, significant variation within the same class, and notable similarities
between different classes. This paper introduces a novel class-wise attention
technique that equally regards each class while unearthing more specific
details about skin lesions. This attention mechanism is progressively used to
amalgamate discriminative feature details from multiple scales. The introduced
technique demonstrated impressive performance, surpassing more than 15
cutting-edge methods including the winners of HAM1000 and ISIC 2019
leaderboards. It achieved an impressive accuracy rate of 97.40% on the HAM10000
dataset and 94.9% on the ISIC 2019 dataset.
