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arXiv · 2504.20435

AI Assisted Cervical Cancer Screening for Cytology Samples in Developing Countries

Love Panta·Suraj Prasai·Karishma Malla Vaidya·Shyam Shrestha·Suresh Manandhar
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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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Love Panta, Suraj Prasai, Karishma Malla Vaidya, Shyam Shrestha, Suresh Manandhar. 2025-04-29. AI Assisted Cervical Cancer Screening for Cytology Samples in Developing Countries. https://arxiv.org/abs/2504.20435

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