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BO-CLAHE增强新生儿胸部X射线图像质量,以改善病变分类

Jiwon Han¹, Byungmin Choi², Jae Young Kim³

¹Department of Applied Artificial Intelligence, Hanyang University, Seoul, 15588, Korea.

Scientific reports

|February 10, 2025

概括

提高新生儿的便携式X射线图像质量至关重要. 我们的贝叶斯优化CLAHE (BO-CLAHE) 方法增强图像以更好的AI诊断,减少高风险婴儿的辐射暴露.

科学领域:

医疗成像医学成像
医疗保健中的人工智能
新生儿护理新生儿护理

背景情况:

便携式X射线对于诊断新生儿,特别是早产和高危婴儿至关重要.
便携式X射线的图像质量往往不理想,阻碍了准确的异常检测和AI模型训练.
提高图像质量至关重要,但由于新生儿的敏感性,增加辐射剂量是不可行的.

研究的目的:

开发一种用于优化新生儿便携式X射线图像增强技术的自动化方法.
提高X射线图像的质量,以提高早产和高风险新生儿的诊断准确度.
促进新生儿肺部疾病可靠的基于人工智能的诊断工具的开发.

主要方法:

拟议的贝叶斯优化CLAHE (BO-CLAHE) 用于对比度有限的自适应式直方形平衡 (CLAHE) 的自动超参数调整.
应用BO-CLAHE来增强新生儿的便携式X射线图像.
使用各种深度学习分类模型评估增强图像的性能.

主要成果:

博克莱显著提高了人工智能模型在分类新生儿肺部疾病方面的性能.
在诊断新生儿暂时性气喘 (TTN) 中观察到显著的改善.
与标准增强方法相比,增强图像导致了更高的分类准确性.

结论:

波克莱提供了一种高效和有效的解决方案,用于增强新生儿便携式X射线图像.
这种方法减少了手动超参数调整的需要,节省了时间和资源.
该方法支持减少辐射暴露,并推进人工智能驱动的诊断,以改善新生儿结果.

关键词:

贝叶斯优化是贝叶斯的优化.克拉赫 (Clahe) 是一种调味料.高风险新生儿高风险新生儿新生儿胸部X射线新生儿胸部X射线过早生育的孩子.

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