A Wavelet Guided Attention Module for Skin Cancer Classification with Gradient-based Feature Fusion

Dermoscopy requires a welltrained physician with experience and visual ability, while Skin cancer is a highly dangerous type of cancer that requires skin biopsy involves taking a sample of skin from the patient's an accurate diagnosis from experienced physicians. To help body, which can be a slow and painful process. These difficulties physicians diagnose skin cancer more efficiently, a computeraided have spurred researchers in the field of artificial intelligence diagnosis (CAD) system can be very helpful. In this (AI) to create Computer-Aided Diagnosis (CAD) systems paper, we propose a novel model, which uses a novel attention capable of precise skin cancer classification. However, mechanism to pinpoint the differences in features across due to the inter-class similarity and intra-class dissimilarity the spatial dimensions and symmetry of the lesion, thereby focusing among different types of skin cancer, classifying skin cancer on the dissimilarities of various classes based on symmetry, using medical image processing is a challenging issue.