A brain-inspired computational framework for image-based risk assessment

Skin cancer risk prediction plays a critical role in early diagnosis and personalized healthcare. However, existing approaches often struggle to achieve both rich feature representation and computational efficiency. To address these issues, this study proposes Bicom, a brain-inspired framework for skin cancer risk prediction that integrates efficient attention mechanisms, multi-scale feature fusion, and confidence-aware refinement. Specifically, we develop F-ResNeSt, a multi-scale feature-extraction architecture that enhances the ResNeSt backbone by incorporating a Feature Pyramid Network (FPN) and Linformer-based linear-complexity attention. In addition, we propose L-CoAtNet, an optimized classification network that replaces conventional relative attention with Linformer-based attention to achieve scalable global contextual modeling. Furthermore, a brain-inspired Spiking Neural Network (SNN) module is introduced as a confidence-aware refinement mechanism to enhance prediction reliability for ambiguous samples. Comprehensive experiments on public and subject datasets demonstrate that the proposed framework achieves consistently competitive performance across multiple evaluation metrics, indicating its effectiveness, robustness, and scalability for assisted skin cancer risk prediction.