Progressive Class-Wise Attention (PCA) Approach for Diagnosing Skin Lesions
This work addresses early detection of skin cancer, a critical global health issue, by improving classification accuracy for skin lesions, though it appears incremental as it builds on existing attention mechanisms.
The paper tackles the problem of classifying skin lesions, which is challenging due to variations in appearance and similarities between classes, by introducing a novel class-wise attention technique that integrates discriminative features from multiple scales, achieving accuracies of 97.40% on HAM10000 and 94.9% on ISIC 2019 datasets.
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.