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In India, cervical cancer is the second highest type of cancer in women after breast cancer. Due to few nonspecific symptoms in early stages routine screening by Pap smear of targeted population can be very useful for the early diagnosis of cervical cancer. So, in this paper, a detailed exploration of Recurrent Neural Networks (RNNs) for the classification of cervical cancer cell images. Through meticulous evaluation of learning dynamics, we offer insights into the model’s learning capability, generalization, and potential interventions to optimize performance. The results demonstrate the model’s exemplary accuracy is 97.14% with 100 epochs, highlighting its potential as a robust tool for biomedical image analysis.

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Utilizing Recurrent Neural Networks for Cervical Cancer Cell Classification Using Pap Smear Images

  • 会议论文

  • 发表日期：

    2025年

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  • Mithlesh Arya,
  • Praveen Kumar Yadav,
  • Megha Gupta,
  • Abha Jain

摘要

In India, cervical cancer is the second highest type of cancer in women after breast cancer. Due to few nonspecific symptoms in early stages routine screening by Pap smear of targeted population can be very useful for the early diagnosis of cervical cancer. So, in this paper, a detailed exploration of Recurrent Neural Networks (RNNs) for the classification of cervical cancer cell images. Through meticulous evaluation of learning dynamics, we offer insights into the model’s learning capability, generalization, and potential interventions to optimize performance. The results demonstrate the model’s exemplary accuracy is 97.14% with 100 epochs, highlighting its potential as a robust tool for biomedical image analysis.