从使用创新的深度学习模型的X射线图像对肺部疾病的诊断贡献
Akram Bennour1, Najib Ben Aoun2,3, Osamah Ibrahim Khalaf4
1LAMIS Laboratiry, Echahid Cheikh Larbi Tebessi University, Tebessa, Algeria.
Heliyon
|May 6, 2024
概括
深度学习模型通过胸部X射线准确检测COVID-19,肺炎和肺不透明. 这些人工智能工具显著提高了关键肺部疾病的诊断准确性.
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:
- 准确的肺病诊断至关重要,但具有挑战性,放射性解释存在困难.
- 人工智能 (AI),特别是机器学习 (ML) 和深度学习 (DL),在医疗图像分析方面表现有前途.
- 使用放射学识别肺部疾病的现有方法需要提高准确性和效率.
研究的目的:
- 开发和评估深度学习模型,以从胸部X光学中识别特定的肺部疾病.
- 创建能够区分正常和患有肺部疾病的AI系统,包括COVID-19,肺炎和肺不透明.
- 将新型深度学习模型的性能与肺部疾病检测当前最先进方法进行比较.
主要方法:
- 提出了三个不同的深度学习模型:CovCXR-Net,MDCXR3-Net和MDCXR4-Net.
- 在胸部放射数据集上训练模型,以分类各种肺部疾病.
- 使用已建立的基准和准确度指标评估模型性能.
主要成果:
- 在识别COVID-19或正常病例方面,CovCXR-Net实现了99.09%的准确性.
- 在区分COVID-19,肺炎或正常情况下,MDCXR3-Net的准确率达到了97.74%.
- 在分类COVID-19,肺炎,肺不透明或正常情况方面,MDCXR4-Net获得了90.37%的准确性.
- 与现有的最先进的方法相比,所有拟议的模型都表现出优异的性能.
结论:
- 深度学习模型提供了一个非常准确和有效的方法来通过胸部X射线诊断肺部疾病.
- 开发的AI系统CovCXR-Net,MDCXR3-Net和MDCXR4-Net在临床环境中显示出快速可靠的疾病识别的巨大潜力.
- 这些发现凸显了人工智能在提高肺部疾病诊断的准确性和效率方面的变革性影响.
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