FS-YOLOv9:基于频率和空间特征的YOLOv9,用于实时检测乳腺癌
Haitian Gui1, Tao Su2, Xinhua Jiang3
1School of Biomedical Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen 518107, China (H.G.).
Academic radiology
|October 15, 2024
概括
一个新的YOLOv9模型FS-YOLOv9显著提高了乳腺癌检测准确度. 这种增强的模型显示了早期乳腺癌查和诊断中临床使用的巨大潜力.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 乳腺癌查对于降低死亡率至关重要.
- 实时物体检测模型为癌症查应用提供了潜力.
- 现有的模型需要针对不同乳腺癌特征进行优化.
研究的目的:
- 开发和评估一个定制的YOLOv9模型,用于增强乳腺癌检测.
- 提高基于人工智能的乳腺癌查的准确性和稳定性.
- 评估一个优化的物体检测模型对乳腺癌诊断的临床意义.
主要方法:
- 开发了一个定制的FS-YOLOv9模型,包含一个最大聚合层和Haar波束卷积内核.
- 在687个案件的内部数据集上训练并验证了模型,并在98个外部案例上进行了测试.
- 使用F1分数,FAUC,mAP,回忆和精度评估模型性能,与YOLOv9,YOLOv8和YOLOv5.5进行比较.
主要成果:
- 在内部数据集上,FS-YOLOv9的表现优于官方YOLOv9模型,平均F1得分高 (0.700比0.669) 和FAUC高 (0.695比0.662).
- 在外部测试数据集上,FS-YOLOv9与官方YOLOv9.9相比,F1得分提高了4.58%,FAUC提高了5.78%,mAP50提高了4.41%.
- 改进的重点是改善检测高亮度特征和与乳腺癌相关的形态/纹理特征.
结论:
- 开发的FS-YOLOv9模型在乳腺癌检测方面显著提高了性能.
- 定制模型在高风险乳腺癌诊断方面显示出实际实用性.
- 这种人工智能方法有望促进早期检测和改善乳腺癌查患者的结果.
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