Department of Gynecology and Obstetrics, Fujian Medical University 2nd Affiliated Hospital, Quanzhou, China.
College of Engineering, Huaqiao University, Quanzhou, China.
J Clin Ultrasound. 2024 Jul-Aug;52(6):753-762. doi: 10.1002/jcu.23703. Epub 2024 Apr 27.
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.
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.
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.
By using this deep learning hybrid approach, we address the shortcomings of the current classification methods of uterine fibroid.
子宫肌瘤（UF）是女性最常见的肿瘤之一，可能对流产等并发症构成巨大威胁。医生经验不足和疲劳也会影响预后的准确性，这凸显了对能够从大量不同图像中分析 UF 的自动分类模型的需求。
提出了一种混合模型，将 MobileNetV2 网络和深度卷积生成对抗网络（DCGAN）结合起来，为医生识别 UF 和评估其特征提供有用的资源。实时自动分类 UF 有助于诊断病情并最大程度减少主观错误。利用 DCGAN 技术进行高级统计增强，创建优质的 UF 图像，并将其标记为 UF 和非子宫肌瘤（NUF）类别。然后，MobileNetV2 模型根据这些数据对图像进行精确分类。
混合模型的整体性能与其他模型进行对比。混合模型实现了 40 帧/秒（FPS）的实时分类速度、97.45%的准确率和 0.9741 的 F1 评分。
通过使用这种深度学习混合方法，我们解决了当前子宫肌瘤分类方法的缺点。
