Multimodal-multiscale hybrid fusion network for automated esophageal cancer diagnosis.

ABSTRACT: Accurate and early diagnosis of esophageal cancer (EC) remains challenging due to heterogeneous imaging characteristics and the complexity of integrating multimodal data. This study proposes a multimodal-multiscale hybrid fusion network (MHF-Net), which combines domain-driven handcrafted features (HFs) and deep multiscale features (DFs) from multimodal data for the automated diagnosis of EC. MHF-Net uses a dilated-inception block to extract multiscale representations at varied dilation rates and concatenates these DF with domain-specific features via dense connections. A convolutional block attention module further refined spatial and channel-wise features, leveraging global average pooling. The grasshopper optimization algorithm optimized fusion weights and hyperparameters, enhancing hybr

SUBMITTER: Lonseko ZM 

PROVIDER: S-EPMC12818140 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature