A Wavelet Guided Attention Module for Skin Cancer Classification with Gradient-based Feature Fusion
This work addresses the problem of accurate skin cancer diagnosis for physicians, offering an incremental improvement in computer-aided diagnosis systems.
The paper tackled skin cancer classification by proposing a model with a novel attention mechanism and gradient-based feature fusion, achieving 91.17% F1-score and 90.75% accuracy on the HAM10000 dataset.
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.