Cervical Cytology Classification Using Multiple CNN Architectures with Transformer-Based Feature Enhancement

Cervical cancer is the fourth most common cancer among women worldwide and remains a major public health challenge, particularly in regions with limited access to early screening and diagnosis. Accurate classification of cervical cytology images is critical for early detection of cervical cancer, which remains a major health burden in low- and middle-income countries. This study presents a comprehensive evaluation of multiple deep learning architectures for the automated classification of Pap smear images into four diagnostic categories: Negative for Intraepithelial Lesion or Malignancy (NILM), Low-Grade Squamous Intraepithelial Lesion (LSIL), High-Grade Squamous Intraepithelial Lesion (HSIL), and Squamous Cell Carcinoma (SCC). We systematically compare eight Convolutional Neural Network (CNN) architectures: AlexNet, VGG-16, GoogLe-Net, Network-in-Network (NIN), ResNet-50, DenseNet-121, Capsule Networks, and EfficientNet-B0 on a publicly available cervical cytology dataset. To enhance feature representation and capture long-range dependencies, we additionally incorporate a Vision Transformer (ViT-16) model. All models are trained and evaluated under identical preprocessing and sampling conditions to ensure fair benchmarking. Experimental results demonstrate that ViT-16 achieves the highest test accuracy of 95.88% and an overall specificity of 0.9864, outperforming all CNN counterparts. EfficientNet-B0 and DenseNet-121 also showed strong performance, achieving 94.33% and 93.30% accuracy, respectively. Notably, ViT-16 provided superior classification outcomes for challenging minority classes such as SCC and HSIL. The findings highlight the growing potential of transformer-based models in cytopathology and underscore the importance of architectural design in developing robust diagnostic tools. This work contributes a comparative foundation for future research in AI-assisted cervical cancer screening systems.