Efficient Breast and Ovarian Cancer Classification via ViT-Based Preprocessing and Transfer Learning

Richa Rawat; Faisal Ahmed

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arXiv · 2509.18553

Efficient Breast and Ovarian Cancer Classification via ViT-Based Preprocessing and Transfer Learning

Richa Rawat·Faisal Ahmed

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Abstract

Cancer is one of the leading health challenges for women, specifically breast and ovarian cancer. Early detection can help improve the survival rate through timely intervention and treatment. Traditional methods of detecting cancer involve manually examining mammograms, CT scans, ultrasounds, and other imaging types. However, this makes the process labor-intensive and requires the expertise of trained pathologists. Hence, making it both time-consuming and resource-intensive. In this paper, we introduce a novel vision transformer (ViT)-based method for detecting and classifying breast and ovarian cancer. We use a pre-trained ViT-Base-Patch16-224 model, which is fine-tuned for both binary and multi-class classification tasks using publicly available histopathological image datasets. Further, we use a preprocessing pipeline that converts raw histophological images into standardized PyTorch tensors, which are compatible with the ViT architecture and also help improve the model performance. We evaluated the performance of our model on two benchmark datasets: the BreakHis dataset for binary classification and the UBC-OCEAN dataset for five-class classification without any data augmentation. Our model surpasses existing CNN, ViT, and topological data analysis-based approaches in binary classification. For multi-class classification, it is evaluated against recent topological methods and demonstrates superior performance. Our study highlights the effectiveness of Vision Transformer-based transfer learning combined with efficient preprocessing in oncological diagnostics.

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Richa Rawat, Faisal Ahmed. 2025-09-23. Efficient Breast and Ovarian Cancer Classification via ViT-Based Preprocessing and Transfer Learning. https://arxiv.org/abs/2509.18553

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