基于人工智能技术的CT扫描上的COVID-19检测和诊断模型
Maria-Alexandra Zolya1, Cosmin Baltag1, Dragoș-Vasile Bratu1
1Department of Automatics and Information Technology, Transilvania University of Brasov, 500036 Brașov, Romania.
Bioengineering (Basel, Switzerland)
|January 22, 2024
概括
一个深度学习模型有效地从CT扫描中分类COVID-19,病毒性肺炎和健康的肺部. 移动NetV1在检测肺异常方面表现出高精度,有助于医学诊断.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 肺部病理学 肺部病理学
背景情况:
- 随着COVID-19的爆发,人们越来越需要快速,准确的诊断工具.
- 肺部CT扫描对于诊断呼吸系统疾病至关重要,但解释可能耗时.
- 过度劳累的临床医生需要自动化解决方案来进行高效的医学图像分析.
研究的目的:
- 开发一个标准化,基于深度学习的肺部CT扫描报告系统.
- 将患者分为三个类别:COVID-19,病毒性肺炎或健康.
- 通过医学成像和人工智能来解决分类呼吸系统疾病的挑战.
主要方法:
- 从CT扫描中提取肺腔,然后进行数据增强.
- 卷积神经网络 (CNN) 的开发用于二进制 (COVID与非COVID) 和三类分类.
- 利用转移学习,将定制线性分类器集成到预先训练的模型中,如MobileNetV1,ResNet50,DenseNet201,VGG16和VGG19.
主要成果:
- 使用CAM技术,MobileNetV1实现了使用CAM技术对肺异常的卓越检测准确度.
- 该模型表现出高性能,准确度为0.9744,精度为0.9758,回忆率为0.9742,AUC为0.9902,F1得分为0.9750.
- 在每个类别的1161个样本中获得了优异的分类结果,用于COVID-19,病毒性肺炎和健康个体.
结论:
- 深度学习,特别是CNN和转移学习,为自动肺部CT扫描分析提供了一个有前途的解决方案.
- MobileNetV1显示了准确检测COVID-19和病毒性肺炎的巨大潜力,即使在现有文献中没有广泛使用.
- 开发的系统可以帮助有效和准确的诊断,可能减轻临床医生的工作负担.
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