从二进制到多类分类:基于X射线图像的胸部疾病分类的两步混合CNN-ViT模型
Yousra Hadhoud1, Tahar Mekhaznia1, Akram Bennour1
1LAMIS Laboratory, Larbi Tebessi University, Tebessa 12002, Algeria.
Diagnostics (Basel, Switzerland)
|December 17, 2024
概括
结合卷积神经网络 (CNN) 和视觉转换器 (ViT) 的新混合模型准确检测结核病,并从胸部X射线中区分肺炎类型. 这种计算机辅助诊断 (CAD) 系统显示出高精度,有助于资源有限的设置.
科学领域:
- 医疗成像中的人工智能
- 深度学习用于诊断系统
- 放射图像分析 放射图像分析
背景情况:
- 胸部疾病的鉴定,特别是结核病和肺炎,面临的诊断挑战,由于重叠的X光学特征.
- 专家放射科医生的有限可用性加剧了诊断困难,特别是在发展中国家.
- 需要对胸部X射线图像进行客观和一致的分析,以减少诊断中的人为错误.
研究的目的:
- 开发一种计算机辅助诊断 (CAD) 系统,用于分析胸部X射线图像.
- 使用混合AI模型准确检测结核病并区分结核病和肺炎.
- 利用卷积神经网络 (CNN) 和视觉转换器 (ViT) 的优势,提高诊断性能.
主要方法:
- 设计了一个两步混合模型,将ResNet-50 CNN与ViT-b16架构集成在一起.
- 转移学习是使用广州妇女和儿童医疗中心 (肺炎) 和卡塔尔/达卡大学 (肺结核) 的数据集进行的.
- 该模型将CNN的层次特征提取与ViT的自我注意机制相结合,以改善分类.
主要成果:
- 混合CNN-ViT模型在结核病检测的二进制分类中实现了98.97%的准确性.
- 对于多类分类 (结核病,病毒性肺炎,细菌性肺炎),该模型达到96.18%的准确性.
- 这些结果表明,在胸部疾病分类中,改善诊断准确性和可靠性的巨大潜力.
结论:
- 拟议的混合CNN-ViT模型显示了在胸部疾病诊断中推进CAD系统的巨大潜力.
- 整合CNN和ViT架构提高了诊断精度,为复杂的放射分析提供了强大的解决方案.
- 这种方法可以减轻资源有限的环境中的医疗负担,并改善患者对胸部疾病的治疗结果.
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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...
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