提高COVID-19检测:一个基于Xception的模型,从X射线胸部图像中获得高级转移学习
Reagan E Mandiya1,2, Hervé M Kongo1, Selain K Kasereka1,2
1Mathematics, Statistics and Computer Science Department, University of Kinshasa, Kinshasa XI P.O. Box 190, Democratic Republic of the Congo.
Journal of imaging
|March 27, 2024
概括
本研究引入了一种先进的Xception模型,用于从胸部X射线检测2019年新冠肺炎 (COVID-19) 的转移学习. 与现有模型相比,新方法显著提高了诊断准确性.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 传染病诊断 传染病诊断 传染病诊断
背景情况:
- 准确和快速的冠状病毒疾病2019 (COVID-19) 检测对于患者管理和流行病控制至关重要.
- 现有的深度学习模型,特别是卷积神经网络 (CNN),在医学图像分析中面临着过度装配和高计算成本等挑战.
- 需要使用放射图像进行COVID-19诊断的更有效,更准确的AI模型.
研究的目的:
- 开发和评估一种创新的深度学习模型,以从胸部X射线图像中增强COVID-19检测.
- 解决现有模型的局限性,包括表现力问题和资源密集型培训.
- 提高人工智能驱动的COVID-19诊断工具的准确性和效率.
主要方法:
- 利用Xception架构,一个最先进的CNN,用于图像分类.
- 整合了先进的转移学习技术,以增强Xception模型的性能.
- 在用于COVID-19识别的胸部X射线图像数据集上训练并验证了模型.
主要成果:
- 与VGG-16和ResNet.Net等基线模型相比,提议的Xception模型与转移学习显示出更高的预测准确性.
- 该模型有效地从胸部X射线图像中识别了COVID-19病例,超过了既定的方法.
- 实验结果表明,诊断性能提高,并有可能克服常见的深度学习挑战.
结论:
- 开发的转移学习增强的Xception模型在基于AI的X射线COVID-19检测方面取得了重大进展.
- 这种方法代表了朝着更准确,更有效,更容易获得的传染病诊断工具迈出的有希望的一步.
- 这些发现表明,在流行病期间改善患者治疗结果和公共卫生监测的可行解决方案.
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Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...