Automatic screening system for conventional Pap smear analysis
Year: 2026
Type: article
Abstract: 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.
Authors: David Kupas, Ilona Kovacs, Andras Hajdu, Balazs Harangi
Institution: University of Debrecen
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,590
Award: G7525121970
OpenAlex ID: W7162944158