YOLO算法提高了乳房镜的诊断性能:不仅仅是眼睛
Heng Zhang1, Xiao Yang2, Leilei Yuan3
1Department of Radiology, Jining No. 1 People's Hospital, China.
概括
这项研究开发了一种用于乳腺癌 (BC) 检测的计算机辅助诊断 (CAD) 系统. 集成的CAD系统在检测和分类乳房镜中的病变方面显示出高精度,有助于早期诊断.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 乳腺癌 (BC) 是全球女性中最常见的恶性瘤.
- 早期检测和准确的诊断对于提高患者存活率至关重要.
研究的目的:
- 开发一个集成的计算机辅助诊断 (CAD) 系统,用于自动化病变检测,细分和分类的乳房图像.
- 提高乳腺癌 (BC) 的诊断过程.
主要方法:
- 使用YOLOv5作为对象检测模型.
- 采用了选乳腺成像数据库的数字数据库查乳房扫描 (CBIS-DDSM) 数据集的精选乳房成像子集.
- 应用数据增强技术,并将数据分成培训,验证和测试集 (7:2:1比率).
主要成果:
- 在0.5信心值 (mAP@0.5) 达到0.417的平均平均精度和0.46的F1评分用于病变检测.
- 获得了0.90的接收器操作特征 (ROC) 曲线 (AUC) 下的面积,用于区分良性和恶性病变.
- 该系统以每秒65 (fps) 的速度处理图像.
结论:
- 综合CAD系统在乳腺癌病变检测和分类方面展示了快速处理和高精度的结合.
- 该系统在改善乳腺癌诊断方面具有显著的临床应用潜力.
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