Citation

Hasan, Md Jahid and Wan Ahmad, Wan Siti Halimatul Munirah and Ahmad Fauzi, Mohammad Faizal and Abas, Fazly Salleh and Cheah, Phaik Leng and Chiew, Seow Fan and Looi, Lai Meng (2026) GEM-Net: a compact Ghost-ECA CNN with multi-stage feature fusion for multi-domain histopathology classification. Journal of King Saud University Computer and Information Sciences, 38 (4). ISSN 1319-1578

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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) Ultra-Lightweight Ghost modules, which generate redundant feature maps inexpensively, (ii) a Micro-ECA attention mechanism, which adaptively calibrates channel responses using minimal parameters, and (iii) depthwise separable convolutions within compact residual Micro-Blocks, reducing computation while preserving representational power. Unlike prior lightweight models that apply efficiency modules independently, GEM-Net adopts an attention-regulated feature-economy design that tightly couples lightweight feature generation, capacity-aware channel recalibration, and compensatory multi-stage feature fusion. A knowledge distillation strategy further compresses the model, transferring knowledge from a 972.3K-parameter teacher to a 29.26K-parameter student with negligible accuracy loss. Extensive evaluations on five datasets–HER2-IHC (private), CRC100K, Kather-texture-2016, Chaoyang, and LC25000– demonstrate state-of-the-art or near state-of-the-art performance. GEM-Net achieves perfect accuracy on LC25000 and over 96% accuracy on HER2-IHC, CRC100K, and Kather, while outperforming most models on the challenging Chaoyang dataset. GEM-Net scales different variants for edge and server deployment. In addition, explainable AI techniques, including GradCAM++ and Integrated Gradients, provide interpretable visual and attribution-based explanations, enhancing transparency and clinical trust.

Item Type:	Article
Uncontrolled Keywords:	Medical image, analysis, digital pathology, deep learning
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	05 May 2026 07:46
Last Modified:	08 May 2026 06:38
URII:	http://shdl.mmu.edu.my/id/eprint/15895

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