ECAViT-Net: A Lightweight Hybrid CNN-Transformer Architecture for Efficient Cervical Cytological Cell Classification

Cervical cancer, primarily caused by human papillomavirus (HPV) infection, remains a major cause of cancer-related mortality among women worldwide, making early detection through cytological screening essential. However, manual analysis of cytology images is time-consuming and subject to variability, while recent deep learning approaches, particularly transformer-based architectures, often require high computational resources, limiting their use in resource-constrained settings. In this study, we propose ECAViTNet, a lightweight hybrid CNN–Transformer architecture for cervical cytology image classification that balances accuracy and efficiency. The model integrates Efficient Channel Attention modules for adaptive feature recalibration, residual connections for stable optimization, MobileViT blocks to capture local and global dependencies, and gated multi-scale fusion mechanisms to enhance feature representation, along with progressive downsampling and skip connections to preserve fine-grained details. The proposed approach was evaluated on the SIPaKMeD dataset, achieving a test accuracy of 96.42% with only 982,491 parameters and a macro-average F1-score of 0.96 and a weighted-average F1-score of 0.96, while maintaining balanced class-wise performance and reduced computational cost compared to recent methods. These results demonstrate that ECAViTNet is an effective and efficient solution for automated cervical cytology classification, with strong potential for deployment in mobile health systems and low-resource clinical environments.