Transformer-assisted broad learning for hybrid intelligence-based skin cancer segmentation.

ABSTRACT: With the rise of Transformer architectures, deep learning applications have gradually shifted from traditional convolutional neural networks to Transformers based on self-attention mechanisms. In tasks such as image classification, segmentation, and detection, pure Transformer models (such as ViT, Swin Transformer, PVT) and hybrid architectures (such as ConViT, BoTNet) have become mainstream. At the same time, Broad Learning Systems (BLS) exhibit unique advantages in real-time scenarios with limited resources and small sample sizes, thanks to efficient training, strong dynamic scalability, and global optimization mechanisms. However, BLS has shortcomings in feature design, computational complexity, representation capability, and incremental learning stability. Transformer architectures eff

SUBMITTER: Cai Y 

PROVIDER: S-EPMC12514299 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature