Vision Transformer optimized on Herlev dataset reaches 94.9-95.2% accuracy in cervical cell classification with Grad-CAM attention aligning to nuclear and chromatin features.
Hybridization of Deep Learning Pre-Trained Models with Machine Learning Classifiers and Fuzzy Min–Max Neural Network for Cervical Cancer Diagnosis
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Systematic Evaluation of Vision Transformers for Automated Cervical Cancer Classification: Optimization, Statistical Validation, and Clinical Interpretability
Vision Transformer optimized on Herlev dataset reaches 94.9-95.2% accuracy in cervical cell classification with Grad-CAM attention aligning to nuclear and chromatin features.