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<p>Chinese herbal slices (CHS) serve as the core carrier of Traditional Chinese Medicine, and their identification represents the primary step in quality control and medication safety. Because herbal slices exhibit a variety of morphological forms, some species share similar morphological traits, making precise identification a significant challenge. Existing algorithms have a problem: they can’t capture enough global dependencies, and their local feature discriminability is not sufficient in fine-grained CHS recognition. To address these problems, we propose SFE-CA, a unified fine-grained classification framework that synergistically integrates Coordinate Attention (CA) and Sequential Feature Enhancement (SFE). Unlike conventional methods that treat architecture and optimization separately, SFE-CA incorporates a customized dual-loss optimization strategy as an intrinsic component, designed to maximize the discriminative power of the SFE-extracted features. This holistic design allows the model to address the inter-class confusion issues that arise from highly similar target categories. Feature discrimination further improves through multi-task optimization with dual losses. On a 14-category herbal slice dataset, SFE-CA achieves significant performance, with 98.91% accuracy and 99.00% F1-score. The experimental results indicate that the proposed framework outperforms existing methods, which exhibit superior discriminative capacity for morphologically similar specimens.</p>

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SFE-CA: omni feature aware modelling integrating coordinate attention and sequential feature enhancement for chinese herbal slices classification

  • 期刊论文

  • 发表日期：

    2026年5月6日

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  • Siyu Zhang,
  • Qingyu Yan,
  • Qingmei Guo,
  • Fengqin Zhou,
  • Jinyu Cong,
  • Xiang Li,
  • Kunmeng Liu,
  • Pingping Wang,
  • Benzheng Wei

摘要

Chinese herbal slices (CHS) serve as the core carrier of Traditional Chinese Medicine, and their identification represents the primary step in quality control and medication safety. Because herbal slices exhibit a variety of morphological forms, some species share similar morphological traits, making precise identification a significant challenge. Existing algorithms have a problem: they can’t capture enough global dependencies, and their local feature discriminability is not sufficient in fine-grained CHS recognition. To address these problems, we propose SFE-CA, a unified fine-grained classification framework that synergistically integrates Coordinate Attention (CA) and Sequential Feature Enhancement (SFE). Unlike conventional methods that treat architecture and optimization separately, SFE-CA incorporates a customized dual-loss optimization strategy as an intrinsic component, designed to maximize the discriminative power of the SFE-extracted features. This holistic design allows the model to address the inter-class confusion issues that arise from highly similar target categories. Feature discrimination further improves through multi-task optimization with dual losses. On a 14-category herbal slice dataset, SFE-CA achieves significant performance, with 98.91% accuracy and 99.00% F1-score. The experimental results indicate that the proposed framework outperforms existing methods, which exhibit superior discriminative capacity for morphologically similar specimens.