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<p>Correct histopathological image classification of lung and colon cancer is a stringent challenge for clinical pathology. This work introduces a hybrid deep learning network by combining traditional handcrafted features of LBP, GLCM, wavelet, color, and morphological descriptors with deep features derived from an extended EfficientNetB0. A transformer-based attention fusion strategy is adopted to fuse these heterogeneous representations, facilitating robust multi-scale feature learning. To even better accommodate adaptability and curtail catastrophic forgetting, the model is trained with an adaptive incremental learning approach with stage-wise data augmentation. The suggested method is trained on the LC25000 dataset and tested on two public, independent datasets, NCT-CRC-HE-100K and HMU-GC-HE-30K, showing consistent performance with accuracies of 99.87%, 99.07%, and 98.4%, respectively. These findings are affirmations of the framework’s generalizability, scalability, and clinical applicability in multi-class histopathological image classification. All source code and dataset access instructions are publicly made available to encourage reproducibility and extension.</p>

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Fusion of classical and deep learning features with incremental learning for improved classification of lung and colon cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月19日

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  • Mullakuri Anusha,
  • D. Srinivasulu Reddy

摘要

Correct histopathological image classification of lung and colon cancer is a stringent challenge for clinical pathology. This work introduces a hybrid deep learning network by combining traditional handcrafted features of LBP, GLCM, wavelet, color, and morphological descriptors with deep features derived from an extended EfficientNetB0. A transformer-based attention fusion strategy is adopted to fuse these heterogeneous representations, facilitating robust multi-scale feature learning. To even better accommodate adaptability and curtail catastrophic forgetting, the model is trained with an adaptive incremental learning approach with stage-wise data augmentation. The suggested method is trained on the LC25000 dataset and tested on two public, independent datasets, NCT-CRC-HE-100K and HMU-GC-HE-30K, showing consistent performance with accuracies of 99.87%, 99.07%, and 98.4%, respectively. These findings are affirmations of the framework’s generalizability, scalability, and clinical applicability in multi-class histopathological image classification. All source code and dataset access instructions are publicly made available to encourage reproducibility and extension.