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Detection of Hysteroscopic Hysteromyoma in Real-Time Based on Deep Learning

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单位： [1]School of Computer Engineering and Science, Shanghai University, Shanghai, China [2]School of Computer Engineering and Science, Shanghai University, Shanghai, China [3]Department of Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China [4]Department of Gynecology, Hubei Maternal and Child Health Hospital, Wuhan, Hubei, China [5]University of Aizu, Aizu, Fukushima, Japan [6]School of Computer Engineering and Science, Shanghai University, Shanghai, China [7]Department of Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China

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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. © Published under licence by IOP Publishing Ltd.

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第一作者单位： [1]School of Computer Engineering and Science, Shanghai University, Shanghai, China

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