Systematic Evaluation of Vision Transformers for Automated Cervical Cancer Classification: Optimization, Statistical Validation, and Clinical Interpretability

In three linesSystematic optimization of Vision Transformers (ViT-Tiny) for cervical cancer screening on Herlev dataset (917 images). Optimal configuration: 94.9%-95.2% cross-validation accuracy with horizontal flipping and class weighting (0.7 x 1.3). Grad-CAM validates clinical interpretability: attention on nuclei, cell boundaries, and chromatin texture.

Summary generated by Claude — human-verified