Application of EfficientNet-B0 and GRU-based deep learning on classifying the colposcopy diagnosis of precancerous cervical lesions.

Journal: Cancer medicine
Published Date: Apr 1, 2023

Abstract

BACKGROUND: Colposcopy is indispensable for the diagnosis of cervical lesions. However, its diagnosis accuracy for high-grade squamous intraepithelial lesion (HSIL) is at about 50%, and the accuracy is largely dependent on the skill and experience of colposcopists. The advancement in computational power made it possible for the application of artificial intelligence (AI) to clinical problems. Here, we explored the feasibility and accuracy of the application of AI on precancerous and cancerous cervical colposcopic image recognition and classification.

Authors

  • Xiaoyue Chen
    Department of Gynecology, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, China.
  • Xiaowen Pu
    Department of Gynecology, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, China.
  • Zhirou Chen
    Department of Gynecology, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, China.
  • Lanzhen Li
    Department of Automation, Shanghai Jiao Tong University, Shanghai, China.
  • Kong-Nan Zhao
    School of Basic Medical Science, Wenzhou Medical University, Wenzhou, China.
  • Haichun Liu
    Laboratory of Molecular Design and Drug Discovery, School of Science, China; Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198 Jiangsu, China.
  • Haiyan Zhu
    Department of Gynecology, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, China.

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