HistoViT: Vision Transformer for Accurate and Scalable Histopathological Cancer Diagnosis

• URL: http://arxiv.org/abs/2508.11181v1
• Date: Fri, 15 Aug 2025 03:10:52 GMT
• Title: HistoViT: Vision Transformer for Accurate and Scalable Histopathological Cancer Diagnosis
• Authors: Faisal Ahmed,
• Abstract summary: We propose a transformer-based deep learning framework for multi-class tumor classification.<n>Our method addresses key limitations of conventional convolutional neural networks.<n>Our approach classification achieves accuracies of 99.32%, 96.92%, 95.28%, and 96.94% for breast, prostate, bone, and cervical cancers respectively.
• Score: 1.5939351525664014
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate and scalable cancer diagnosis remains a critical challenge in modern pathology, particularly for malignancies such as breast, prostate, bone, and cervical, which exhibit complex histological variability. In this study, we propose a transformer-based deep learning framework for multi-class tumor classification in histopathological images. Leveraging a fine-tuned Vision Transformer (ViT) architecture, our method addresses key limitations of conventional convolutional neural networks, offering improved performance, reduced preprocessing requirements, and enhanced scalability across tissue types. To adapt the model for histopathological cancer images, we implement a streamlined preprocessing pipeline that converts tiled whole-slide images into PyTorch tensors and standardizes them through data normalization. This ensures compatibility with the ViT architecture and enhances both convergence stability and overall classification performance. We evaluate our model on four benchmark datasets: ICIAR2018 (breast), SICAPv2 (prostate), UT-Osteosarcoma (bone), and SipakMed (cervical) dataset -- demonstrating consistent outperformance over existing deep learning methods. Our approach achieves classification accuracies of 99.32%, 96.92%, 95.28%, and 96.94% for breast, prostate, bone, and cervical cancers respectively, with area under the ROC curve (AUC) scores exceeding 99% across all datasets. These results confirm the robustness, generalizability, and clinical potential of transformer-based architectures in digital pathology. Our work represents a significant advancement toward reliable, automated, and interpretable cancer diagnosis systems that can alleviate diagnostic burdens and improve healthcare outcomes.

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