Automated Cervical Cancer Screening Using Single-Cell Segmentation and Deep Learning: Enhanced Performance with Liquid-Based Cytology
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 re...
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MDPI AG
2024-11-01
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| Series: | Computation |
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| Online Access: | https://www.mdpi.com/2079-3197/12/12/232 |
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| author | Mariangel Rodríguez Claudio Córdova Isabel Benjumeda Sebastián San Martín |
| author_facet | Mariangel Rodríguez Claudio Córdova Isabel Benjumeda Sebastián San Martín |
| author_sort | Mariangel Rodríguez |
| collection | DOAJ |
| description | 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 (0.981), specificity (0.979), and accuracy (0.980), outperforming previous CNN models. However, the Pap smear dataset model achieved significantly lower performance (0.688 sensitivity, 0.762 specificity, 0.8735 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. |
| format | Article |
| id | doaj-art-b579e3762f5a4b98bd651e66d2951d6a |
| institution | DOAJ |
| issn | 2079-3197 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-b579e3762f5a4b98bd651e66d2951d6a2025-08-20T02:53:43ZengMDPI AGComputation2079-31972024-11-01121223210.3390/computation12120232Automated Cervical Cancer Screening Using Single-Cell Segmentation and Deep Learning: Enhanced Performance with Liquid-Based CytologyMariangel Rodríguez0Claudio Córdova1Isabel Benjumeda2Sebastián San Martín3PhD Program in Health Sciences and Engineering, Faculty of Medicine-Engineering-Sciences, Universidad de Valparaíso, Viña del Mar 2540064, ChileCenter of Interdisciplinary Biomedical and Engineering Research for Health (MEDING), School of Medicine, Faculty of Medicine, Universidad de Valparaíso, Viña del Mar 2540064, ChileDepartment of Sciences, Faculty of Liberal Arts, Adolfo Ibañez University, Viña del Mar 2200055, ChileCenter of Interdisciplinary Biomedical and Engineering Research for Health (MEDING), School of Medicine, Faculty of Medicine, Universidad de Valparaíso, Viña del Mar 2540064, ChileCervical 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 (0.981), specificity (0.979), and accuracy (0.980), outperforming previous CNN models. However, the Pap smear dataset model achieved significantly lower performance (0.688 sensitivity, 0.762 specificity, 0.8735 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.https://www.mdpi.com/2079-3197/12/12/232cervical cancerdeep learningcell segmentationpap smearliquid-based cytology |
| spellingShingle | Mariangel Rodríguez Claudio Córdova Isabel Benjumeda Sebastián San Martín Automated Cervical Cancer Screening Using Single-Cell Segmentation and Deep Learning: Enhanced Performance with Liquid-Based Cytology Computation cervical cancer deep learning cell segmentation pap smear liquid-based cytology |
| title | Automated Cervical Cancer Screening Using Single-Cell Segmentation and Deep Learning: Enhanced Performance with Liquid-Based Cytology |
| title_full | Automated Cervical Cancer Screening Using Single-Cell Segmentation and Deep Learning: Enhanced Performance with Liquid-Based Cytology |
| title_fullStr | Automated Cervical Cancer Screening Using Single-Cell Segmentation and Deep Learning: Enhanced Performance with Liquid-Based Cytology |
| title_full_unstemmed | Automated Cervical Cancer Screening Using Single-Cell Segmentation and Deep Learning: Enhanced Performance with Liquid-Based Cytology |
| title_short | Automated Cervical Cancer Screening Using Single-Cell Segmentation and Deep Learning: Enhanced Performance with Liquid-Based Cytology |
| title_sort | automated cervical cancer screening using single cell segmentation and deep learning enhanced performance with liquid based cytology |
| topic | cervical cancer deep learning cell segmentation pap smear liquid-based cytology |
| url | https://www.mdpi.com/2079-3197/12/12/232 |
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