图像分辨率对卷积神经网络在乳房超声波中的影响
Shuzhen Tang1, Chen Jing1,2, Yitao Jiang3
1Second Clinical College of Jinan University, Shenzhen 518020, Guangdong, China.
Heliyon
|September 4, 2023
概括
这项研究使用各种图像分辨率对乳腺癌诊断的卷积神经网络 (CNN) 进行了比较. 像MobileNet这样的轻量级CNN显示了与更重型号相比的性能,预测时间更快.
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医学成像.
- 深度学习模型为自动诊断辅助提供了潜力.
研究的目的:
- 评估卷积神经网络 (CNN),特别是移动网络和DenseNet121,用于乳腺癌诊断.
- 为了确定不同输入图像分辨率对诊断性能的影响.
主要方法:
- 从2015年6月到2020年11月的2D超声波乳房图像的回顾分析.
- 在图像分辨率从64x64到512x512像素之间比较MobileNet和DenseNet121的性能.
主要成果:
- 移动网络在320x320像素实现了最佳的乳腺癌诊断.
- 在448x448像素时,DenseNet121表现出了高峰性能.
结论:
- 图像分辨率与乳腺癌诊断准确度有显著的相关性.
- 在超声波成像中,轻型CNN (LW-CNNs) 可以匹配或超过较重型号 (HW-CNNs) 的性能.
- LW-CNNs提供了减少每张图像的预测时间.
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