SkinCareNet with DermGraph: Revolutionizing Skin Cancer Detection with Adaptive Graph Intelligence Algorithms

Skin cancer detection presents significant challenges, especially in accurately distinguishing between different types of lesions. To improve detection accuracy, developed SkinCareNet with DermGraph, which addresses the limitations of traditional methods by using Graph Neural Networks (GNNs) to model skin lesions as graph structures. This approach captures complex spatial and relational patterns within the lesion data, going beyond the capabilities of conventional image-based techniques like Convolutional Neural Networks (CNNs). By integrating meta-learning, the model quickly adapts to new datasets with minimal retraining, and advanced data augmentation through graph embeddings enhances its robustness. Additionally, node importance analysis improves interpretability, highlighting key areas influencing the model's decisions. Our system achieved a 98% accuracy in detecting skin cancer, offering a highly reliable and adaptable solution for clinical use.