用人工智能增强的超声波成像用于甲状腺结节检测和恶性瘤分类:YOLOv11的研究
Jiaqi Yang1, Zhigang Luo2, Yanting Wen3
1Operating Room, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, China.
Quantitative imaging in medicine and surgery
|September 2, 2025
概括
您只看一次v. 11 (YOLOv11) 模型在超声波图像中检测和分类甲状腺结节的准确性很高. 这种人工智能工具提高了临床医生的诊断效率,
科学领域:
- 医学成像
- 医学的人工智能
- 癌症学
背景情况:
- 甲状腺结节很常见, 需要准确的诊断才能有效治疗.
- 传统的超声波诊断依赖于医生的经验,
- 医疗成像中的人工智能可以提高诊断准确性和效率.
研究的目的:
- 评估甲状腺结节检测和分类的你只看一次v. 11 (YOLOv11) 模型.
- 评估YOLOv11在超声波图像分析中的有效性.
- 支持实时临床决策和改善诊断工作流程.
主要方法:
- 在1503张甲状腺超声波图像上使用YOLOv11模型 (742张良性,778张恶性).
- 使用数据增强和转移学习进行模型优化.
- 与其他YOLO变体和ResNet50相比,YOLOv11的性能.
主要成果:
- 与其他YOLO变种和ResNet50相比,YOLOv11表现出更高的性能.
- 在IOU0.5时,YOLOv11达到0.841的精度 (P) 和0.823的回忆 (R).
- 对于良性和恶性结节,YOLOv11显示了高的P和R值,性能因IOU值而异.
结论:
- YOLOv11是一种强大的甲状腺结节检测和恶性瘤分类工具.
- 该模型提供高精度和实时性能,对于动态超声波至关重要.
- 未来的工作将扩展数据集,并在实时环境中验证临床实用性.
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