DLAAD-深度学习算法使用放射医学图像协助诊断胸部疾病
Mosleh Hmoud Al-Adhaileh1, Bayan M Alsharbi2, Theyazn H H Aldhyani3
1Deanship of E-Learning and Distance Education and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia.
Frontiers in medicine
|March 24, 2025
概括
这项研究开发了一种深度学习系统,用于通过X射线诊断肺炎,达到92%的准确性. 这种新的方法增强了医学成像和计算机辅助诊断系统的胸部疾病.
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:
- 通过胸部X射线诊断肺炎是具有挑战性的,因为症状与其他呼吸系统疾病重叠.
- 目前用于肺炎的诊断技术的准确性有限.
- 将现代技术整合到医疗设备中对于推进胸部疾病诊断至关重要.
研究的目的:
- 提高计算机辅助诊断系统 (CAD) 对胸部疾病的效率和准确性.
- 开发一种深度学习模型,用于将胸部X射线图像分类为肺炎或正常.
- 为了利用转移学习进行增强的医学图像分析.
主要方法:
- 使用移动NetV2,VGG-16和ResNet50V2模型,采用了一个转移学习方法.
- 采用了5863张胸部X射线图像的数据集,图像的大小改为224x224像素.
- 数据增强和规范化技术应用于深度学习模型,以防止过度拟合和提高性能.
主要成果:
- 开发的系统在从胸部X射线图像中识别肺炎时达到92%的准确性.
- 与其他测试模型相比,具有规范分类头的MobileNetV2模型表现出优异的性能.
- 拟议的技术比现有的诊断胸部疾病的方法更准确.
结论:
- 深度学习系统为肺部疾病诊断的医学成像提供了显著的进步.
- 这项研究为人工智能驱动的诊断系统的未来发展提供了基础.
- 该研究强调了深度学习的潜力,以提高肺炎检测的准确性和效率.
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