在超声波成像中优化YOLOv8以增强乳腺瘤细分
Ayman Mohamed Mostafa1, Alaa S Alaerjan2, Bader Aldughayfiq3
1Information Systems Department, College of Computer and Information Sciences, Jouf University, Sakaka, 72388, Saudi Arabia. amhassane@ju.edu.sa.
Discover oncology
|June 19, 2025
概括
在单独的良性和恶性乳腺瘤类别上训练机器学习模型显著提高了超声波图像细分的准确性. 专业培训可以提高诊断能力,从而改善患者的治疗结果.
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:
- 乳腺癌的诊断严重依赖于超声波图像的准确解释.
- 在超声波图像中手动细分瘤是主观的,耗时的.
- 机器学习 (ML) 为客观和高效的图像分析提供了潜力.
研究的目的:
- 为了比较ML模型对乳房超声波图像细分的有效性,当训练组合与单独的瘤类别时.
- 评估包括YOLOv8在内的各种深度学习 (DL) 模型的性能.
- 研究专业培训策略对诊断准确性的影响.
主要方法:
- 使用了780张乳房超声波图像 (良性和恶性) 的数据集.
- 进行了深度学习模型 (UNet,UNet-DenseNet-121,VGG16,VGG19,适应YOLOv8) 的培训.
- 模型使用两种方法进行评估:对综合数据集的培训和对单独的良性和恶性数据集的培训.
- 性能被评估使用子系数,交叉点在联盟 (IoU) 和平均平均精度 (mAP).
主要成果:
- 与组合训练相比,在单独的瘤类别上的训练模型显著改善了细分性能.
- 联网模型显示F1得分从77.80%增加到84.09%,子系数从75.58%增加到81.17%.
- 经过调整的YOLOv8模型实现了F1得分从93.44%提高到95.29%,子系数从82.10%提高到84.40%.
结论:
- 专业的训练策略,专注于不同的瘤类别,提高ML模型在乳房超声波细分中的性能.
- 像YOLOv8这样的先进的对象检测算法显示了提高细分精度的前景.
- 医学成像分析的这些进步可以导致更准确的诊断和改善患者的治疗结果.
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