Relation Explore Convolutional Block Attention Module for Skin Lesion Classification

Abstract

Skin cancer remains a significant global health concern, demanding accurate and efficient diagnostic solutions. Despite advances in convolutional neural networks for computer vision, automated skin lesion diagnosis remains challenging due to the small lesion region in images and limited inter-class variation. Accurate classification depends on precise lesion localization and recognition of fine-grained visual differences. To address these challenges, this paper proposes an enhancement to the Convolutional Block Attention Module, referred to as Relation Explore Convolutional Block Attention Module. This enhancement improves upon the existing module by utilizing multiple combinations of pooling-based attentions, enabling the model to better learn and leverage complex interactions during training. Extensive experiments are conducted to investigate the performance of skin lesion diagnosis when integrating Relation Explore Convolutional Block Attention Module with ResNet50 at different stages. The best-performing model achieves outstanding classification results on the publicly available HAM10000 dataset, with an Accuracy of 97.63%, Precision of 88.98%, Sensitivity of 82.86%, Specificity of 97.65%, and F1-score of 85.46%, using fivefold cross-validation. The high performance of this model, alongside the clear interpretability provided by its attention maps, builds trust in automated systems. This trust empowers clinicians to make well-informed decisions, significantly enhancing the potential for improved patient outcomes.