GEM-Net: a compact Ghost-ECA CNN with multi-stage feature fusion for multi-domain histopathology classification

Abstract Deep learning has achieved progress in medical image analysis, yet high computation of CNN and Transformer models limits deployment. This study introduces GEM-Net, a novel ultra-lightweight architecture for histopathological image classification. GEM-Net integrates three key innovations: (i...

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Detalhes bibliográficos

Principais autores:	Md Jahid Hasan, Wan Siti Halimatul Munirah Wan Ahmad, Mohammad Faizal Ahmad Fauzi, Fazly Salleh Abas, Phaik-Leng Cheah, Seow-Fan Chiew, Lai-Meng Looi
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Springer 2026-02-01
Colecção:	Journal of King Saud University: Computer and Information Sciences
Assuntos:	Histopathological image classification Lightweight deep learning Computational efficiency Medical image analysis Digital pathology Deep learning
Acesso em linha:	https://doi.org/10.1007/s44443-026-00490-3
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