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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.20435 |
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| _version_ | 1866913812170407936 |
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| author | Panta, Love Prasai, Suraj Vaidya, Karishma Malla Shrestha, Shyam Manandhar, Suresh |
| author_facet | Panta, Love Prasai, Suraj Vaidya, Karishma Malla Shrestha, Shyam Manandhar, Suresh |
| contents | Cervical cancer remains a significant health challenge, with high incidence and mortality rates, particularly in transitioning countries. Conventional Liquid-Based Cytology(LBC) is a labor-intensive process, requires expert pathologists and is highly prone to errors, highlighting the need for more efficient screening methods. This paper introduces an innovative approach that integrates low-cost biological microscopes with our simple and efficient AI algorithms for automated whole-slide analysis. Our system uses a motorized microscope to capture cytology images, which are then processed through an AI pipeline involving image stitching, cell segmentation, and classification. We utilize the lightweight UNet-based model involving human-in-the-loop approach to train our segmentation model with minimal ROIs. CvT-based classification model, trained on the SIPaKMeD dataset, accurately categorizes five cell types. Our framework offers enhanced accuracy and efficiency in cervical cancer screening compared to various state-of-art methods, as demonstrated by different evaluation metrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_20435 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI Assisted Cervical Cancer Screening for Cytology Samples in Developing Countries Panta, Love Prasai, Suraj Vaidya, Karishma Malla Shrestha, Shyam Manandhar, Suresh Computer Vision and Pattern Recognition Cervical cancer remains a significant health challenge, with high incidence and mortality rates, particularly in transitioning countries. Conventional Liquid-Based Cytology(LBC) is a labor-intensive process, requires expert pathologists and is highly prone to errors, highlighting the need for more efficient screening methods. This paper introduces an innovative approach that integrates low-cost biological microscopes with our simple and efficient AI algorithms for automated whole-slide analysis. Our system uses a motorized microscope to capture cytology images, which are then processed through an AI pipeline involving image stitching, cell segmentation, and classification. We utilize the lightweight UNet-based model involving human-in-the-loop approach to train our segmentation model with minimal ROIs. CvT-based classification model, trained on the SIPaKMeD dataset, accurately categorizes five cell types. Our framework offers enhanced accuracy and efficiency in cervical cancer screening compared to various state-of-art methods, as demonstrated by different evaluation metrics. |
| title | AI Assisted Cervical Cancer Screening for Cytology Samples in Developing Countries |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.20435 |