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<p>Breast cancer diagnosis remains a crucial challenge in medical research, necessitating accurate and automated detection methods. This study introduces an advanced deep learning framework for histopathological image classification, integrating AlexNet and Gated Recurrent Unit (GRU) networks, optimized using the Hippopotamus Optimization Algorithm (HOA). Initially, DenseNet-41 extracts intricate spatial features from histopathological images. These features are then processed by the hybrid AlexNet-GRU model, leveraging AlexNet’s robust feature extraction and GRU’s sequential learning capabilities. HOA is employed to fine-tune hyperparameters, ensuring optimal model performance. The proposed approach is evaluated on benchmark datasets (BreakHis and BACH), achieving a classification accuracy of 99.60%, surpassing existing state-of-the-art models. The results demonstrate the efficacy of integrating deep learning with bio-inspired optimization techniques in breast cancer detection. This research offers a robust and computationally efficient framework for improving early diagnosis and clinical decision-making, potentially enhancing patient outcomes.</p>

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Histopathological image based breast cancer diagnosis using deep learning and bio inspired optimization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月30日

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  • Venkata Nagaraju Thatha,
  • M. Ganesh Karthik,
  • Venu Gopal Gaddam,
  • D. Pramodh Krishna,
  • S. Venkataramana,
  • Kranthi Kumar Lella,
  • Udayaraju Pamula

摘要

Breast cancer diagnosis remains a crucial challenge in medical research, necessitating accurate and automated detection methods. This study introduces an advanced deep learning framework for histopathological image classification, integrating AlexNet and Gated Recurrent Unit (GRU) networks, optimized using the Hippopotamus Optimization Algorithm (HOA). Initially, DenseNet-41 extracts intricate spatial features from histopathological images. These features are then processed by the hybrid AlexNet-GRU model, leveraging AlexNet’s robust feature extraction and GRU’s sequential learning capabilities. HOA is employed to fine-tune hyperparameters, ensuring optimal model performance. The proposed approach is evaluated on benchmark datasets (BreakHis and BACH), achieving a classification accuracy of 99.60%, surpassing existing state-of-the-art models. The results demonstrate the efficacy of integrating deep learning with bio-inspired optimization techniques in breast cancer detection. This research offers a robust and computationally efficient framework for improving early diagnosis and clinical decision-making, potentially enhancing patient outcomes.