基于深度学习的皮肤损伤分类:高频超声成像的CNN方法
Isabela Rocha Veiga da Silva1, André Gonçalves Jardim1, Giulia Rita de Souza Faés1
1Federal University of Health Sciences of Porto Alegre, Porto Alegre, Brazil.
概括
深度学习模型使用高频超声波 (HFUS) 图像准确地分类皮肤病变. 结合B模式和多普勒超声数据,可提高诊断性能,用于非侵入性损伤评估.
科学领域:
- 医学成像医学成像
- 皮肤病学中的人工智能
- 超声波技术的超声波技术
背景情况:
- 高频超声波 (HFUS) 对皮肤病变的评估至关重要,有助于诊断,治疗监测和手术规划.
- 精确的皮肤病变分类对于有效的患者管理至关重要.
研究的目的:
- 评估使用HFUS图像对皮肤病变进行二进制分类的深度学习模型.
- 为了比较在B模式和多普勒HFUS数据上训练的模型的性能,单独和组合.
主要方法:
- 开发和培训用于HFUS图像分类的卷积神经网络 (CNN).
- 使用单输入CNN单独用于B模式和多普勒模式.
- 实现 Unity 和 Cascade 架构,以集成 B 模式和多普勒数据.
主要成果:
- 该HFUS-多普勒模型表现出优异的性能,准确率为95.0%,AUC为0.98.
- 统一架构实现了90.5%的准确性和0.97.9的AUC.
- 布架构在分类恶性预测方面显示了较低的准确性,但更高的信心.
结论:
- 结合B模式和多普勒HFUS数据可以提高皮肤病变分类的诊断性能.
- 深度学习模型的有效性取决于网络架构和数据质量.
- 定制的深度学习方法显示出对基于HFUS的非侵入性皮肤病变分析的希望.
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