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<p>Colorectal cancer (CRC) poses a significant global health burden, where early and accurate diagnosis is vital to improving patient outcomes. However, the structural complexity of CRC histopathological images renders manual analysis time-consuming and error-prone. This study aims to develop an automated deep learning framework that enhances classification accuracy and efficiency in CRC diagnosis. The proposed model integrates domain-specific transfer learning and multi-model feature fusion to address challenges such as multi-scale structures, noisy labels, class imbalance, and fine-grained subtype classification. The model first applies domain-specific transfer learning to extract highly relevant features from histopathological images. A multi-head self-attention mechanism then fuses features from multiple pre-trained models, followed by a multilayer perceptron (MLP) classifier for final prediction. The framework was evaluated on three publicly available CRC datasets: EBHI, Chaoyang, and COAD. The model achieved a classification accuracy of 99.68% on the EBHI dataset (200 × subset), 86.72% on the Chaoyang dataset, and 99.44% on the COAD dataset. These results demonstrate strong generalization across diverse and complex histopathological image conditions. This study highlights the effectiveness of combining domain-specific transfer learning with multi-model feature fusion and attention mechanisms for CRC classification. The proposed model offers a reliable and efficient tool to support pathologists in diagnostic workflows, with the potential to reduce manual workload and improve diagnostic consistency.</p>

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Histopathological classification of colorectal cancer based on domain-specific transfer learning and multi-model feature fusion

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月8日

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  • Qi Ke,
  • Yan Chai Hum,
  • Wun-She Yap,
  • Tian Swee Tan,
  • Humaira Nisar,
  • Hamam Mokayed,
  • AiQuan Li,
  • Rong Gao,
  • YuJian Gan

摘要

Colorectal cancer (CRC) poses a significant global health burden, where early and accurate diagnosis is vital to improving patient outcomes. However, the structural complexity of CRC histopathological images renders manual analysis time-consuming and error-prone. This study aims to develop an automated deep learning framework that enhances classification accuracy and efficiency in CRC diagnosis. The proposed model integrates domain-specific transfer learning and multi-model feature fusion to address challenges such as multi-scale structures, noisy labels, class imbalance, and fine-grained subtype classification. The model first applies domain-specific transfer learning to extract highly relevant features from histopathological images. A multi-head self-attention mechanism then fuses features from multiple pre-trained models, followed by a multilayer perceptron (MLP) classifier for final prediction. The framework was evaluated on three publicly available CRC datasets: EBHI, Chaoyang, and COAD. The model achieved a classification accuracy of 99.68% on the EBHI dataset (200 × subset), 86.72% on the Chaoyang dataset, and 99.44% on the COAD dataset. These results demonstrate strong generalization across diverse and complex histopathological image conditions. This study highlights the effectiveness of combining domain-specific transfer learning with multi-model feature fusion and attention mechanisms for CRC classification. The proposed model offers a reliable and efficient tool to support pathologists in diagnostic workflows, with the potential to reduce manual workload and improve diagnostic consistency.