AI Assisted Cervical Cancer Screening for Cytology Samples in Developing Countries

• URL: http://arxiv.org/abs/2504.20435v1
• Date: Tue, 29 Apr 2025 05:18:59 GMT
• Title: AI Assisted Cervical Cancer Screening for Cytology Samples in Developing Countries
• Authors: Love Panta, Suraj Prasai, Karishma Malla Vaidya, Shyam Shrestha, Suresh Manandhar,
• Abstract summary: Cervical cancer remains a significant health challenge, with high incidence and mortality rates.<n> Conventional Liquid-Based Cytology(LBC) is a labor-intensive process, requires expert pathologists and is highly prone to errors.<n>This paper introduces an innovative approach that integrates low-cost biological microscopes with our simple and efficient AI algorithms for automated whole-slide analysis.
• Score: 0.18472148461613155
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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