Automatic ovarian tumors recognition system based on ensemble convolutional neural network with ultrasound imaging.

Journal: BMC medical informatics and decision making
Published Date: Nov 17, 2022

Abstract

BACKGROUND: Upon the discovery of ovarian cysts, obstetricians, gynecologists, and ultrasound examiners must address the common clinical challenge of distinguishing between benign and malignant ovarian tumors. Numerous types of ovarian tumors exist, many of which exhibit similar characteristics that increase the ambiguity in clinical diagnosis. Using deep learning technology, we aimed to develop a method that rapidly and accurately assists the different diagnosis of ovarian tumors in ultrasound images.

Authors

  • Shih-Tien Hsu
    Department of Obstetrics, Gynecology and Women's Health, Taichung Veterans General Hospital, No. 1650 Sec. 4 Taiwan Blvd. Xitun Dist., Taichung, 407, Taiwan.
  • Yu-Jie Su
    Master's Program of Biomedical Infomatics and Biomedical Engineering, Feng Chia University, No. 100 Wenhua Rd. Xitun Dist., Taichung, 407, Taiwan.
  • Chian-Huei Hung
    Department of Obstetrics, Gynecology and Women's Health, Taichung Veterans General Hospital, No. 1650 Sec. 4 Taiwan Blvd. Xitun Dist., Taichung, 407, Taiwan.
  • Ming-Jer Chen
    Department of Obstetrics, Gynecology and Women's Health, Taichung Veterans General Hospital, No. 1650 Sec. 4 Taiwan Blvd. Xitun Dist., Taichung, 407, Taiwan.
  • Chien-Hsing Lu
    Department of Obstetrics and Gynecology, Taichung Veteran General Hospital, Taichung, Taiwan.
  • Chih-En Kuo

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