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Updated: Jun 26, 2025
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使用YOLOv5网络区分生产阿尔多素的腺瘤和非功能性上腺皮腺瘤
Zeyu Piao1,2, Mingzhu Meng2, Huijie Yang3
1Graduate College, Dalian Medical University, Dalian, PR China.
Acta radiologica (Stockholm, Sweden : 1987)
|May 20, 2024
概括
在CT扫描上,YOLOv5深度学习算法准确地区分了阿尔多生成腺瘤 (APA) 和非功能性上腺皮腺瘤 (NF-ACA). YOLOv5x模型展示了最高的识别性能,展示了其在临床诊断中的潜力.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 在瘤学瘤学.
背景情况:
- 深度学习 (DL) 算法如你只看一次版本5 (YOLOv5) 提供了对象检测和分类的高级功能.
- 精确区分上腺意外瘤对于适当的患者管理至关重要.
研究的目的:
- 通过计算机断层扫描 (CT) 图像评估使用YOLOv5算法用于非侵入性分化阿尔多生成腺瘤 (APA) 和非功能性上腺皮腺瘤 (NF-ACA) 的可行性.
主要方法:
- 对235名上腺皮层腺瘤 (ACA) 患者的数据集进行了回顾性分析.
- 患者被分为训练 (90%) 和验证 (10%) 组,另外还有一组20名患者的独立测试组.
- 五个YOLOv5子模型 (YOLOv5n,YOLOv5s,YOLOv5m,YOLOv5l和YOLOv5x) 接受了训练并对其性能进行了评估.
主要成果:
- 在测试组中,YOLOv5x子模型实现了最高的平均平均精度 (mAP_0.5) 0.988和mAP_0.5:0.95 0.711.
- YOLOv5n表现出最快的推断速度为2.4ms,使其具有高度效率.
- 所有评估的YOLOv5子模型在区分APA和NF-ACA方面表现出强的表现.
结论:
- 在CT图像上,YOLOv5算法有效地准确和有效地将APA与NF-ACA区分开来.
- YOLOv5x模型显示出卓越的识别性能,突出了其在上腺瘤特征化中的临床应用潜力.
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