This paper presents a comprehensive automatic screening system designed to assist cytological experts in the evaluation of Pap smears for cervical cancer detection. This work presents an end-to-end automatic screening system developed specifically for conventional Pap smears. The goal of the system is to reduce the burden on human experts by providing a reliable, resource-efficient, and accurate solution that can distinguish between normal and diseased smears. The proposed automatic screening system processes whole-slide images by first dividing them into 2000 × 2000 pixel tiles, then applies a YOLO-based localization algorithm to extract individual cells. These cells are subsequently classified by an ensemble of nine deep neural networks, including convolutional and transformer-based architectures, trained on the publicly available APACC dataset. The final stage involves aggregating cell-level predictions across models to generate smear-level features, which are used to train a LightGBM classifier. The system is evaluated on a private dataset of 339 expert-annotated smears collected in a clinical setting. Despite the relatively small number of smears, over 12 million individual cell predictions were utilized, resulting in an average smear-level classification accuracy of 88.8% using cross-validation. These findings demonstrate the feasibility and potential of such automated systems to support existing cervical cancer screening workflows and to enhance diagnostic consistency and throughput.
Kupás et al. (2026) studied this question.