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利用ResNet50和SENet进行增强的脚骨折识别

Hua Wang¹, Jichong Ying², Jianlei Liu²

¹Department of Medical Imaging, Ningbo No. 6 Hospital, Ningbo, China.

BMC musculoskeletal disorders

|April 2, 2024

概括

这项研究引入了一种先进的深度学习模型,用于从X射线图中诊断脚骨折. 具有SENet功能的Adapted ResNet50显示出高精度,提高了诊断速度和精度.

科学领域:

医学成像分析分析医学成像分析
放射学中的人工智能
骨科诊断骨科诊断骨科诊断骨科诊断

背景情况:

脚骨折是常见的伤害,需要准确的诊断.
诊断脚骨折的传统方法有其局限性.
机器学习为提高诊断准确性和速度提供了潜力.

研究的目的:

开发和评估用于脚骨折检测的深度学习模型.
为了比较不同深度学习架构的性能.
用可解释的AI技术来解释模型.

主要方法:

训练有素的深度学习模型,包括SENet的Adapted ResNet50,在放射图像上.
使用准确性,精度和回忆指标评估模型性能.
采用Grad-CAM用于可视化模型决策过程.

主要成果:

调整后的ResNet50与SENet实现了93%的准确性,95%的AUC和92%的回忆.
该模型的性能优于其他经过测试的深度学习架构.
Grad-CAM可视化突出显示了影响诊断决策的关键图像区域.

结论:

与SENet相结合的Adapted ResNet50模型显示了足骨折检测的显著前景.
这种人工智能工具有可能增强传统的诊断方法.
临床实施需要进一步验证和改进.

关键词:

脚骨折脚骨折在脚骨折.深度学习是一种深度学习.这是Grad-CAM.放射图像放射图像放射图像放射图像在 ResNet50 中,ResNet50 提供了更多信息.这就是SENet的意义.

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