Open AccessDetection of Hysteroscopic Hysteromyoma in Real-Time Based on Deep Learning
Author(s) -
Aihua Zhao,
Jian Zhang,
Shixuan Wang,
Yan Wang,
Xin Zhu,
Wenbin Shen,
Wenwen Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1861/1/012079
Subject(s) - randomness , artificial intelligence , uterine fibroids , medicine , computer science , radiology , surgery , mathematics , statistics
Hysteromyoma is the most common benign tumor in women. By the age of 50, 70% of women have one or more uterine fibroids, and about 30% of them have symptoms and need treatment [1] . In hysteroscopic surgery, doctors’ inexperience and fatigue will reduce the accuracy of hysteromyoma diagnosis. In this paper, a hybrid model based on YOLOv3(YOLO) Network and DCGAN network(DCGAN) is proposed to detect hysteromyoma in real time to assist doctors in diagnosis and reduce subjective randomness. The real-time detection speed of the hybrid model reaches 25FPS, and the accuracy rate reaches 91.73%, which meets the requirements of clinical application and improves the diagnosis efficiency of hysteromyoma.