使用基于YOLO-v7模型的诊断宫淋巴瘤与转移学习
Yuegui Wang1, Caiyun Yang1, Qiuting Yang1
1Department of Ultrasound, Zhangzhou Affiliated Hospital to Fujian Medical University, No. 59 North Shengli Road, Zhangzhou, 363000, Fujian, China.
Scientific reports
|May 14, 2024
概括
一个新的基于YOLO-v7的诊断模型准确地从超声波图像中分类宫淋巴腺病. 这种人工智能模型在差异诊断准确性方面明显优于经验丰富的放射科医生.
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:
- 宫淋巴腺病的诊断严重依赖于放射科医生对超声波图像的解释.
- 淋巴结病态的准确差异诊断对于有效的患者管理至关重要.
- 目前的诊断方法可能是主观的,并且在准确度上有所不同.
研究的目的:
- 评估基于YOLO-v7的辅助诊断模型,用于分类宫淋巴腺病变.
- 将模型的诊断性能与经验丰富的放射科医生进行比较.
- 评估模型在区分良性,淋巴瘤和转移性淋巴结中的实用性.
主要方法:
- 使用了宫淋巴腺病超声波图像的数据集.
- YOLO-v7模型是使用PyTorch进行训练和验证的.
- 模型的性能被评估在一个单独的测试集,并与放射科医生评估相比较.
主要成果:
- 该模型在50%的IOU值下实现了96.4%的平均平均精度 (mAP).
- 该模型的分类准确率为96.2% (良性),98.2% (淋巴瘤) 和96.0% (转移性).
- 放射科医生的精度明显较低:65.9% (良性),83.6% (淋巴瘤) 和58.0% (转移性).
结论:
- 基于YOLO-v7的模型在通过超声波对宫淋巴腺病的分类方面表现出高效率.
- 人工智能模型在淋巴结状况的差异诊断方面超过了经验丰富的放射科医生.
- 这种人工智能工具显示出作为改善宫淋巴腺病症评估的辅助诊断辅助工具的潜力.
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