A Novel Method of Enhancing Skin Lesion Diagnosis Using Attention Mechanisms and Weakly-Supervised Learning

Skin cancer remains a growing global health concern, necessitating accurate and timely diagnostic methodologies. The fusion of artificial intelligence (AI) and medical imaging offers a promising avenue for enhancing skin lesion diagnosis. This research explores integrating attention mechanisms and weakly-supervised learning within a custom convolutional neural network (CNN) architecture for improved skin lesion classification. Leveraging the ISIC 2020 Challenge Dataset, which provides multimodal data, including dermoscopic images and clinical metadata, we propose a holistic approach to disease diagnosis. Attention mechanisms enable the model to focus on critical regions within images, potentially enhancing diagnostic accuracy. Weakly-supervised learning augments attention-guided class activation maps, aiding in feature localization. We evaluate our methodology’s effectiveness in skin lesion diagnosis through a comprehensive experimental framework. Our findings suggest that attention mechanisms and weakly-supervised learning are promising in augmenting diagnostic precision, bridging the gap between AI-driven insights and dermatological expertise. As technology advances, this research envisions a future where AI is pivotal in improving skin cancer management and patient outcomes.