Abstract：
OBJECTIVE: Uterine fibroids (UF) are the most frequent tumors in ladies and can pose an enormous threat to complications, such as miscarriage. The accuracy of prognosis may also be affected by way of doctor inexperience and fatigue, underscoring the want for automatic classification fashions that can analyze UF from a giant wide variety of images.
METHODS: A hybrid model has been proposed that combines the MobileNetV2 community and deep convolutional generative adversarial networks (DCGAN) into useful resources for medical practitioners in figuring out UF and evaluating its characteristics. Real-time automated classification of UF can aid in diagnosing the circumstance and minimizing subjective errors. The DCGAN science is utilized for superior statistics augmentation to create first-rate UF images, which are labeled into UF and non-uterine-fibroid (NUF) classes. The MobileNetV2 model then precisely classifies the photos based totally on this data.
RESULTS: The overall performance of the hybrid model contrasts with different models. The hybrid model achieves a real-time classification velocity of 40 frames per second (FPS), an accuracy of 97.45%, and an F1 rating of 0.9741.
CONCLUSIONS: By using this deep learning hybrid approach, we address the shortcomings of the current classification methods of uterine fibroid.
METHODS: A hybrid model has been proposed that combines the MobileNetV2 community and deep convolutional generative adversarial networks (DCGAN) into useful resources for medical practitioners in figuring out UF and evaluating its characteristics. Real-time automated classification of UF can aid in diagnosing the circumstance and minimizing subjective errors. The DCGAN science is utilized for superior statistics augmentation to create first-rate UF images, which are labeled into UF and non-uterine-fibroid (NUF) classes. The MobileNetV2 model then precisely classifies the photos based totally on this data.
RESULTS: The overall performance of the hybrid model contrasts with different models. The hybrid model achieves a real-time classification velocity of 40 frames per second (FPS), an accuracy of 97.45%, and an F1 rating of 0.9741.
CONCLUSIONS: By using this deep learning hybrid approach, we address the shortcomings of the current classification methods of uterine fibroid.
摘要：
目的：子宫肌瘤（UF）是女性最常见的肿瘤，对并发症构成巨大威胁，比如流产。预后的准确性也可能受到医生经验不足和疲劳的影响，强调需要自动分类的方式，可以分析UF从一个巨大的各种各样的图像。
方法：已经提出了一种混合模型，该模型将MobileNetV2社区和深度卷积生成对抗网络（DCGAN）结合为医疗从业者找出UF并评估其特征的有用资源。UF的实时自动分类可以帮助诊断情况并最大程度地减少主观错误。DCGAN科学用于卓越的统计增强，以创建一流的UF图像，将其标记为UF和非子宫肌瘤（NUF）类别。然后，MobileNetV2模型完全基于此数据对照片进行精确分类。
结果：混合模型的整体性能与不同模型形成对比。混合模型实现了40帧每秒（FPS）的实时分类速度，准确率为97.45%,F1等级为0.9741。
结论：通过使用这种深度学习混合方法，针对目前子宫肌瘤分类方法的不足。
方法：已经提出了一种混合模型，该模型将MobileNetV2社区和深度卷积生成对抗网络（DCGAN）结合为医疗从业者找出UF并评估其特征的有用资源。UF的实时自动分类可以帮助诊断情况并最大程度地减少主观错误。DCGAN科学用于卓越的统计增强，以创建一流的UF图像，将其标记为UF和非子宫肌瘤（NUF）类别。然后，MobileNetV2模型完全基于此数据对照片进行精确分类。
结果：混合模型的整体性能与不同模型形成对比。混合模型实现了40帧每秒（FPS）的实时分类速度，准确率为97.45%,F1等级为0.9741。
结论：通过使用这种深度学习混合方法，针对目前子宫肌瘤分类方法的不足。
