Enhanced local feature extraction of lite network with scale-invariant CNN for precise segmentation of small brain tumors in MRI.

ABSTRACT: Deep learning has emerged as the preeminent technique for semantic segmentation of brain MRI tumors. However, existing methods often rely on hierarchical downsampling to generate multi-scale feature maps, effectively capturing fine-grained global features but struggling with large-scale local features due to insufficient network depth. This limitation is particularly detrimental for segmenting diminutive targets such as brain tumors, where local feature extraction is crucial. Augmenting network depth to address this issue leads to excessive parameter counts, incompatible with resource-constrained devices. To tackle this challenge, we propose that object recognition should exhibit scale invariance, so we introduce a shared CNN network architecture for image encoding. The input MRI image is

SUBMITTER: Yuan W 

PROVIDER: S-EPMC12561956 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature