Version 1
: Received: 5 October 2024 / Approved: 5 October 2024 / Online: 7 October 2024 (11:28:45 CEST)
How to cite:
Rodríguez, M.; Córdova, C.; Benjumeda, I.; San Martín, S. Automated Cervical Cancer Screening Using Single-Cell Segmentation and Deep Learning: Enhanced Performance with Liquid-Based Cytology. Preprints2024, 2024100386. https://doi.org/10.20944/preprints202410.0386.v1
Rodríguez, M.; Córdova, C.; Benjumeda, I.; San Martín, S. Automated Cervical Cancer Screening Using Single-Cell Segmentation and Deep Learning: Enhanced Performance with Liquid-Based Cytology. Preprints 2024, 2024100386. https://doi.org/10.20944/preprints202410.0386.v1
Rodríguez, M.; Córdova, C.; Benjumeda, I.; San Martín, S. Automated Cervical Cancer Screening Using Single-Cell Segmentation and Deep Learning: Enhanced Performance with Liquid-Based Cytology. Preprints2024, 2024100386. https://doi.org/10.20944/preprints202410.0386.v1
APA Style
Rodríguez, M., Córdova, C., Benjumeda, I., & San Martín, S. (2024). Automated Cervical Cancer Screening Using Single-Cell Segmentation and Deep Learning: Enhanced Performance with Liquid-Based Cytology. Preprints. https://doi.org/10.20944/preprints202410.0386.v1
Chicago/Turabian Style
Rodríguez, M., Isabel Benjumeda and Sebastián San Martín. 2024 "Automated Cervical Cancer Screening Using Single-Cell Segmentation and Deep Learning: Enhanced Performance with Liquid-Based Cytology" Preprints. https://doi.org/10.20944/preprints202410.0386.v1
Abstract
Cervical cancer (CC) remains a significant health issue, especially in low- and middle-income countries (LMICs). While Pap smears are the standard screening method, they have limitations like low sensitivity and subjective interpretation. Liquid-based cytology (LBC) offers improvements but still relies on manual analysis. This study explored the potential of deep learning (DL) for automated cervical cell classification using both Pap smears and LBC samples. A novel image segmentation algorithm was employed to extract single-cell patches for training a ResNet-50 model.The model trained on LBC images achieved remarkably high sensitivity (0.998), specificity (0.997), and accuracy (0.997), outperforming previous CNN models. However, the Pap smear dataset model achieved significantly lower performance (0.694 sensitivity, 0.838 specificity, 0.834 accuracy). This suggests that noisy and poor cell definition in Pap smears pose challenges for automated classification, whereas LBC provides better classifiable cells patches. These findings demonstrate the potential of AI-powered cervical cell classification for improving CC screening, particularly with LBC. The high accuracy and efficiency of DL models combined with effective segmentation can contribute to earlier detection and more timely intervention. Future research should focus on implementing explainable AI models to increase clinician trust and facilitate the adoption of AI-assisted CC screening in LMICs.
Keywords
cervical cancer; deep learning; cell segmentation; pap smear; liquid-based cytology
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
