Scholarlink

Cervical cancer remains a significant global health challenge, necessitating precise diagnostic techniques. This paper introduces a novel detection system for cervical cell classification based on the Herlev pap-smear dataset. Our approach integrates Non-local Means (NLM) filtering for noise reduction and a Dilated Spatial Pyramid Pooling (DSPP) module to enhance feature extraction. By leveraging deep convolutional neural networks, our model achieves higher accuracy and consistency than traditional techniques. This work contributes to automated cervical cancer screening and scalable clinical applications.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhancing Squamous Cell Carcinoma Detection in Pap Smears Using Multi-scale Feature Refinement with Dilated Spatial Pyramid Pooling

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Medhovarsh Bayapureddi,
  • A. Supreeth Gupta,
  • A. Karthik,
  • A. Sri Harshitha,
  • J. Vinitha Panicker

摘要

Cervical cancer remains a significant global health challenge, necessitating precise diagnostic techniques. This paper introduces a novel detection system for cervical cell classification based on the Herlev pap-smear dataset. Our approach integrates Non-local Means (NLM) filtering for noise reduction and a Dilated Spatial Pyramid Pooling (DSPP) module to enhance feature extraction. By leveraging deep convolutional neural networks, our model achieves higher accuracy and consistency than traditional techniques. This work contributes to automated cervical cancer screening and scalable clinical applications.