An Ensemble-Based Two-Step Framework for Classification of Pap Smear Cell Images
This work addresses the problem of managing increasing pap smear screening workloads for cytologists, but it appears incremental as it builds on existing neural network methods for a specific medical imaging challenge.
The paper tackles automated classification of pap smear cell images to assist cytologists, proposing a two-stage ensemble neural network approach that first filters out unsuitable images and then classifies the rest into healthy, unhealthy, or both categories, though no concrete performance numbers are provided.
Early detection of cervical cancer is crucial for improving patient outcomes and reducing mortality by identifying precancerous lesions as soon as possible. As a result, the use of pap smear screening has significantly increased, leading to a growing demand for automated tools that can assist cytologists managing their rising workload. To address this, the Pap Smear Cell Classification Challenge (PS3C) has been organized in association with ISBI in 2025. This project aims to promote the development of automated tools for pap smear images classification. The analyzed images are grouped into four categories: healthy, unhealthy, both, and rubbish images which are considered as unsuitable for diagnosis. In this work, we propose a two-stage ensemble approach: first, a neural network determines whether an image is rubbish or not. If not, a second neural network classifies the image as containing a healthy cell, an unhealthy cell, or both.