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<p>Over time, screening for cervical cancer reduces the need for more involved and costly treatments for advanced cancer, making them cost-effective. They regulate further diagnostic tests, such as biopsies, to confirm cancer or precancerous diseases, resulting in better patient care. Initial measures may substantially enhance&#xa0;health and reduce healthcare inequities while being less expensive and resource-intensive. This research explores the application of Transformer Vision architectures for screening (TF-ViS) and diagnosis of cervical-cancer (CvC), with a focus on analyzing Pap smear image data. Transformers Vision prominent for utilizing attention mechanisms to discern intricate cellular patterns are employed to differentiate between malignant and normal cells in Pap smear slides. This TF-ViS-CvC study examines the screening and diagnostic accuracy for cervical cancer and the findings indicate that it outperforms conventional methods by obtaining accuracy of 99.23% in detecting and categorizing atypical cells, leading to improved diagnostic precision and potentially reducing false positive rates.</p>

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TF-ViS-CvC: an automated transforming vision based cervical cancer screening with Pap smear analysis

  • 期刊论文

  • 发表日期：

    2025年1月12日

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  • Sudharani B. Banappagoudar,
  • Zabiha Khan,
  • Mathar Mohideen Nagoor Thangam,
  • Bhuneshwari Dash,
  • M. Kalaivani,
  • Saurabh Aggarwal

摘要

Over time, screening for cervical cancer reduces the need for more involved and costly treatments for advanced cancer, making them cost-effective. They regulate further diagnostic tests, such as biopsies, to confirm cancer or precancerous diseases, resulting in better patient care. Initial measures may substantially enhance health and reduce healthcare inequities while being less expensive and resource-intensive. This research explores the application of Transformer Vision architectures for screening (TF-ViS) and diagnosis of cervical-cancer (CvC), with a focus on analyzing Pap smear image data. Transformers Vision prominent for utilizing attention mechanisms to discern intricate cellular patterns are employed to differentiate between malignant and normal cells in Pap smear slides. This TF-ViS-CvC study examines the screening and diagnostic accuracy for cervical cancer and the findings indicate that it outperforms conventional methods by obtaining accuracy of 99.23% in detecting and categorizing atypical cells, leading to improved diagnostic precision and potentially reducing false positive rates.