基于深度学习和粒子群优化的CT扫描和X射线数据集的分类
Honghua Liu1, Mingwei Zhao2, Chang She3
1Hunan University of Chinese Medicine, Changsha, PR China.
PloS one
|January 27, 2025
概括
这项研究引入了一个深度学习模型,以减少从肺部图像中检测COVID-19的虚假阳性. 双阶段优化方法显著提高了医疗图像分类和预警系统的准确性.
科学领域:
- 医学成像分析 医学成像分析
- 医疗保健中的深度学习
- 放射学 放射学是一门学科.
背景情况:
- COVID-19大流行凸显了医疗系统监测和早期预警能力的关键差距.
- 深度学习计算机辅助诊断模型提供了潜在的解决方案,但传统方法在高假阳性率下扎.
- 准确可靠的疾病检测对于有效的公共卫生反应至关重要.
研究的目的:
- 使用肺部图像开发一种新的,低虚假阳性率的COVID-19疾病检测方法.
- 建立一个两阶段的优化模型,以提高医学图像分类的准确性.
- 提高计算机辅助诊断系统对传染病的可靠性.
主要方法:
- 为COVID-19检测开发了一种两阶段的深度学习模型.
- 第一个阶段涉及训练与梯度下降的特征提取.
- 第二阶段专注于使用构造的客观函数来最大限度地降低假阳性率.
主要成果:
- 该模型在减少虚假阳性率方面取得了显著进展,在公共数据集中将虚假阳性率降至11.3%和7.5%.
- 在ROC曲线下的面积 (AUC) 显著增加,达到92.8%和97.01%.
- 拟议的方法在分类COVID-19病例方面实现了高准确性和低虚假阳性率.
结论:
- 拟议的两阶段优化模型有效地对具有低假阳性率的医疗图像进行分类.
- 这种方法显示了改善疾病监测和早期预警系统的潜力.
- 这些发现表明,它是提高放射学和公共卫生领域诊断准确性的宝贵工具.
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