评估基于编码器-解码器的分段模型,用于甲状腺超声波图像
Niranjan Yadav1, Rajeshwar Dass2, Jitendra Virmani3
1Department of Electronics and Communication Engineering, Deenbandhu Chhotu Ram University of Science and Technology Murthal, Sonepat, 131039, India. niranjanyadav97@gmail.com.
Medical & biological engineering & computing
|June 23, 2023
概括
ResNet50模型在分段甲状腺瘤超声波图像方面取得了最佳性能,为实时医疗保健应用提供了甲状腺结节的准确识别.
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:
- 编码-解码模型对于语义细分至关重要,将图像像素分类为感兴趣区域 (ROI) 或背景.
- 甲状腺瘤超声波 (TTUS) 图像的准确细分对于诊断和治疗规划至关重要.
研究的目的:
- 实施和评估各种编码器-解码器语义细分模型用于TTUS图像细分.
- 为了比较不同模型的性能,包括SegNet,U-Net,mobileNetv2,ResNet,Xception和Inception网络.
主要方法:
- 实现了多个编码器-解码器架构 (SegNet,U-Net,mobileNetv2,ResNet,Xception,Inception) 用于TTUS图像分割.
- 利用转移学习在原始和脱光的TTUS图像上训练模型.
- 评估模型性能使用平均交叉在欧盟 (mIoU) 和平均子系数 (mDC) 的指标.
主要成果:
- 基于ResNet50的模型表现出卓越的性能,达到0.87的mIoU和0.94.9的mDC.
- 客观指标和放射科医生的主观评估都证实了ResNet50模型的有效性.
- 该模型准确地细分了病变特征,如形状,边缘和回声性.
结论:
- 该ResNet50语义细分模型为TTUS图像分析提供了高度准确和可靠的性能.
- 该模型显示了将其集成到医疗保健系统中的巨大潜力,以实时识别甲状腺结节.
- 这些发现支持使用先进的深度学习技术来改进医疗图像分析.
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