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In this study, a comprehensive examination of various AI methodologies employed in the automated screening of cervical cancer is presented. By analyzing multiple research papers, the integration of AI methods in cervical cancer screening is evaluated, considering factors such as dataset size, limitations, and accuracy. The paper provides insights into machine learning algorithms like SVM, GLCM, KNN, MARS, CNNs, spatial fuzzy clustering, PNNs, Genetic Algorithm, RFT, C5.0, CART, and hierarchical clustering, utilized for obtaining feature, cell segmentation, and classification. Additionally, publicly available datasets relevant to cervical cancer are discussed. The review highlights the chronological evolution of computational methods for detecting malignant cells, offering a comprehensive understanding of the advancements in this field over time.

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A Historical Overview of Cervical Cancer Screening Algorithms

  • 会议论文

  • 发表日期：

    2025年

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  • Gaurav Kumawat,
  • Puneet Mittal,
  • Santosh Kumar Vishwakarma,
  • Prasun Chakrabarti,
  • Chirag Joshi,
  • Surendra Solanki

摘要

In this study, a comprehensive examination of various AI methodologies employed in the automated screening of cervical cancer is presented. By analyzing multiple research papers, the integration of AI methods in cervical cancer screening is evaluated, considering factors such as dataset size, limitations, and accuracy. The paper provides insights into machine learning algorithms like SVM, GLCM, KNN, MARS, CNNs, spatial fuzzy clustering, PNNs, Genetic Algorithm, RFT, C5.0, CART, and hierarchical clustering, utilized for obtaining feature, cell segmentation, and classification. Additionally, publicly available datasets relevant to cervical cancer are discussed. The review highlights the chronological evolution of computational methods for detecting malignant cells, offering a comprehensive understanding of the advancements in this field over time.