细分或不细分:COVID-19检测用于胸部X射线
Sara Al Hajj Ibrahim1, Khalil El-Khatib1
1Ontario Tech University, Canada.
Informatics in medicine unlocked
|June 22, 2023
概括
人工智能 (AI) 显著增强了医疗成像分析. 然而,与独立的机器学习和深度学习模型相比,用于COVID-19检测的计算机视觉细分技术降低了准确性.
科学领域:
- 医疗成像中的人工智能
- 计算机视觉用于疾病检测和检测.
- 机器学习和深度学习应用程序
背景情况:
- 人工智能 (AI) 越来越多地融入技术,医学成像是关键的应用领域.
- 计算机视觉 (CV) 算法在分析医疗图像和识别模式方面表现有前途.
- 人工智能已经成为最先进的医学成像技术的组成部分,改善了结果.
研究的目的:
- 通过使用胸部X射线 (CXRs) 调查用于COVID-19分析的计算机视觉 (CV) 分段技术.
- 为了比较k-means,U-net和洪水填充细分方法的有效性,用于肺部区域提取.
- 评估机器学习 (ML) 和深度学习 (DL) 模型的性能,以识别COVID-19病变.
主要方法:
- 利用k-means,U-net和洪水填充用于CXRs中的肺部区域细分.
- 采用ML和DL模型来检测健康和病态肺部图像中的COVID-19病变分子.
- 评估ML和DL性能与CV细分技术相结合.
- 经过测试的DL模型对现实世界噪音的强度,包括盐和胡噪音.
主要成果:
- 与直接ML和DL模型相比,CV细分技术的性能较低.
- 最佳的人工智能算法实现了0.92-0.94.4之间的准确性.
- 整合CV算法将准确度降低到大约0.81-0.88.88.
- 现实世界的噪音对DL模型的性能产生了负面影响.
结论:
- 直接应用ML和DL模型在COVID-19病变检测中比使用CV细分预处理更有效.
- 在这种情况下,CV细分技术不会增强AI模型的性能,而且可能会阻碍AI模型的性能.
- 需要进一步的研究来优化人工智能模型,在医学成像中在噪音条件下提供强大的性能.
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