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<p>Breast cancer remains a critical global health challenge, with timely and reliable diagnosis being essential for improving clinical outcomes. Although recent advances in computer aided diagnosis (CAD) have increasingly adopted deep learning, many existing solutions rely on computationally intensive architectures such as Transformer based models, deep ensemble models, multi scale attention networks, DenseNet based frameworks that limit their practical utility. To overcome this limitation, we proposed a comparatively lightweight Reciprocal Gating Fusion framework that integrates two efficient convolutional neural networks, SqueezeNet and ShuffleNetV2, enabling high quality feature extraction with substantially reduced computational overhead. The proposed reciprocal gating mechanism facilitates structured bidirectional interaction between the networks, enhancing complementary feature exchange while suppressing redundant responses to produce a more informative fused representation. Extensive empirical evaluations on benchmark datasets demonstrate strong performance and generalization capability, achieving 97% multiclass accuracy and 99% binary accuracy on the ICIAR-2018 dataset, along with 99.72% accuracy on the BreakHis dataset at 100<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\times\)</EquationSource> </InlineEquation> magnification. These results highlight the effectiveness of the proposed framework in delivering a precise and dependable CAD solution for breast cancer detection. The code is made available at: <a href="https://github.com/Cmatermedicalimageanalysis/RCG_ICIAR_Breakhis">https://github.com/Cmatermedicalimageanalysis/RCG_ICIAR_Breakhis</a></p>

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Reciprocal cooperative gating fusion of SqueezeNet and ShuffleNetV2 for breast cancer detection in histopathology images

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月21日

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  • Britika Khati,
  • Sayan Mukherjee,
  • Aleksandr Sinitca,
  • Dmitrii Kaplun,
  • Ram Sarkar

摘要

Breast cancer remains a critical global health challenge, with timely and reliable diagnosis being essential for improving clinical outcomes. Although recent advances in computer aided diagnosis (CAD) have increasingly adopted deep learning, many existing solutions rely on computationally intensive architectures such as Transformer based models, deep ensemble models, multi scale attention networks, DenseNet based frameworks that limit their practical utility. To overcome this limitation, we proposed a comparatively lightweight Reciprocal Gating Fusion framework that integrates two efficient convolutional neural networks, SqueezeNet and ShuffleNetV2, enabling high quality feature extraction with substantially reduced computational overhead. The proposed reciprocal gating mechanism facilitates structured bidirectional interaction between the networks, enhancing complementary feature exchange while suppressing redundant responses to produce a more informative fused representation. Extensive empirical evaluations on benchmark datasets demonstrate strong performance and generalization capability, achieving 97% multiclass accuracy and 99% binary accuracy on the ICIAR-2018 dataset, along with 99.72% accuracy on the BreakHis dataset at 100 \(\times\) magnification. These results highlight the effectiveness of the proposed framework in delivering a precise and dependable CAD solution for breast cancer detection. The code is made available at: https://github.com/Cmatermedicalimageanalysis/RCG_ICIAR_Breakhis