Integrating SEResNet101 and SE-VGG19 for advanced cervical lesion detection: a step forward in precision oncology.

Journal: BMC cancer
Published Date: May 28, 2025

Abstract

BACKGROUND: Cervical cancer remains a significant global health issue, with accurate differentiation between low-grade (LSIL) and high-grade squamous intraepithelial lesions (HSIL) crucial for effective screening and management. Current methods, such as Pap smears and HPV testing, often fall short in sensitivity and specificity. Deep learning models hold the potential to enhance the accuracy of cervical cancer screening but require thorough evaluation to ascertain their practical utility.

Authors

  • Yan Ye
    Department of Gastroenterology, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
  • Yuanyuan Chen
    Center for Radiation Oncology, Affiliated Hangzhou Cancer Hospital, Zhejiang University School of Medicine, Hangzhou 310001, China.
  • Jiajia Pan
    Department of Obstetrics and Gynecology, The Third Clinical Institute Affiliated to Wenzhou Medical University, Wenzhou People's Hospital, Wenzhou, 325000, China. Electronic address: 188795267@qq.com.
  • Peipei Li
    School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230601, Anhui, China. Electronic address: peipeili@hfut.edu.cn.
  • Feifei Ni
    Department of Gynecological Protection, Wenzhou People's Hospital, Wenzhou, 325000, China.
  • Haizhen He
    Department of Gynecological Protection, Wenzhou People's Hospital, Wenzhou, 325000, China. byzj936@alumni.sjtu.edu.cn.

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