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<p>Cancer remains one of the leading causes of global mortality, with lung, colon, skin, and breast cancers contributing significantly to the disease burden. Accurate and timely classification of histopathological images is critical for effective diagnosis and treatment planning. However, existing deep learning models for histopathology often achieve strong results but remain limited to single-cancer classification, lack generalizability across datasets, and provide little transparency for clinical use. To address these gaps, we propose CancerDet-Net, a comprehensive unified framework capable of classifying nine histopathological subtypes across four major cancer types. CancerDet-Net integrates separable convolutional layers, Vision Transformer (ViT) blocks with local-window sparse self-attention, and a Hierarchical Multi-Scale Gated Attention Mechanism (HMSGA), combined through Cross-Scale Feature (CSF) Fusion. Unlike prior approaches, our model not only achieves top-performing accuracy (98.51%) but also incorporates explainable AI (XAI) visualizations and is deployed via both a web-based platform and Android app for real-time clinical use. This combination of multi-cancer generalization, interpretability, and deployment readiness establishes CancerDet-Net as a distinctive contribution to AI-driven digital pathology.</p>

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Cross-platform multi-cancer histopathology classification using local-window vision transformers

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月19日

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  • Md. Darun Nayeem,
  • Nusrat Jahan Nisita,
  • Md. Masudul Islam,
  • Md. Saifur Rahman,
  • A. B. M. Shawkat Ali

摘要

Cancer remains one of the leading causes of global mortality, with lung, colon, skin, and breast cancers contributing significantly to the disease burden. Accurate and timely classification of histopathological images is critical for effective diagnosis and treatment planning. However, existing deep learning models for histopathology often achieve strong results but remain limited to single-cancer classification, lack generalizability across datasets, and provide little transparency for clinical use. To address these gaps, we propose CancerDet-Net, a comprehensive unified framework capable of classifying nine histopathological subtypes across four major cancer types. CancerDet-Net integrates separable convolutional layers, Vision Transformer (ViT) blocks with local-window sparse self-attention, and a Hierarchical Multi-Scale Gated Attention Mechanism (HMSGA), combined through Cross-Scale Feature (CSF) Fusion. Unlike prior approaches, our model not only achieves top-performing accuracy (98.51%) but also incorporates explainable AI (XAI) visualizations and is deployed via both a web-based platform and Android app for real-time clinical use. This combination of multi-cancer generalization, interpretability, and deployment readiness establishes CancerDet-Net as a distinctive contribution to AI-driven digital pathology.