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在CT扫描中使用注意力U-net与混合损失函数进行COVID-19病变的自动细分

Samy Bakheet¹, Rehab Youssef², Mahmoud H Mofaddel³

¹Department of Computer Science, College of Computer Engineering and Science, Prince Sattam bin Abdulaziz University, Al Kharj, 11942, Saudi Arabia. s.bakheet@psau.edu.sa.

Scientific reports

|January 11, 2026

概括

这项研究引入了一个深度学习框架,在CT扫描中自动细分COVID-19肺炎. 该方法提高了诊断和评估疾病严重程度的准确性,为医学成像提供了一个实用的工具.

科学领域:

医疗成像医学成像
人工智能在医学中的应用
放射学放射学是一门学科.

背景情况:

由于COVID-19的流行,需要有效的诊断工具.
计算机断层扫描 (CT) 对于COVID-19的检测和监测至关重要.
在CT扫描中精确细分肺炎病变对于诊断和严重性评估至关重要,但低对比度的感染区域对自动化方法构成挑战.

研究的目的:

开发一个可访问的深度学习框架,用于CT扫描中COVID-19感染地区的自动细分.
为了应对自动化医疗图像细分中低对比度感染区域的挑战.

主要方法:

实施一个深度学习框架,集成对比度有限的自适应式直方体平衡 (CLAHE) 预处理.
使用训练有混合子-特弗斯基损失函数的注意力U-Net模型.
采用广泛的数据增强技术来改进模型通用化,并应用可解释的人工智能 (XAI) 方法,如梯度加权类激活映射 (Grad-CAM),以提高可解释性.

主要成果:

在公开的COVID-19CT数据集上,实现了0.83的子得分,0.71的交叉点和99.74%的准确性.
证明了CLAHE预处理和注意力U-Net模型与混合损失的有效性.
通过5倍的交叉验证进行验证,证实了强大的性能.

结论:

拟议的深度学习框架在CT扫描中有效地对COVID-19感染地区进行细分.
整合了CLAHE,Attention U-Net和混合损失,提高了细分精度,特别是在对比度低的区域.
该框架显示出作为COVID-19诊断和管理中医学成像分析的实用工具的巨大潜力.

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