集团深度学习架构与机器学习用于肺炎分类使用胸部X射线.
Rupali Vyas1, Deepak Rao Khadatkar2
1Department of Computer Science and Engineering, Shri Shankaracharya Institute of Professional Management and Technology, Raipur, C.G, India. rupalivyas1996@gmail.com.
Journal of imaging informatics in medicine
|August 14, 2024
概括
这项研究引入了与机器学习分类器 (DLxMLCs) 结合的深度学习,以从胸部X射线图像中准确分类肺炎. 该DLxMLC方法实现了超过99%的准确性,提高了诊断速度和效率.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 计算病理学计算病理学
背景情况:
- 肺炎对健康构成重大风险,特别是在脆弱人群中,需要迅速和准确的诊断才能进行有效的治疗.
- 胸部X射线 (CXR) 影像是肺炎的主要诊断工具,但解释可能具有挑战性和耗时.
研究的目的:
- 评估将深度学习 (DL) 模型与机器学习 (ML) 分类器相结合的有效性,用于从CXR图像中自动分类肺炎.
- 评估新型深度学习结合机器学习分类器 (DLxMLC) 方法的诊断准确性和效率.
主要方法:
- 利用修改后的VGG19,ResNet50V2和DenseNet121深度学习模型从CXR图像中自动提取特征.
- 集成的提取特征与五个不同的机器学习分类器:逻辑回归,支持矢量机,决策树,随机森林和人工神经网络.
- 评估了各种DLxMLC组合用于肺炎检测的性能.
主要成果:
- 当VGG19和DenseNet121模型与随机森林或决策树分类器配对时,可以达到99.98%的特殊精度.
- 当与随机森林分类器相结合时,ResNet50V2模型显示了99.25%的准确性.
- 这些高准确度突显了拟议的DLxMLC策略在肺炎识别中的有效性.
结论:
- 深度学习模型与机器学习分类器的集成显著提高了从CXR图像中检测肺炎的速度和准确性.
- DLxMLC系统在改善临床环境中的诊断准确性和操作效率方面具有相当大的潜力.
- 建议进行进一步的研究,以完善这些模型,探索它们在其他医学成像领域的应用,并纳入临床信任和采用的可解释性.
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