使用视觉转换器和可解释AI的增强结核病检测,在胸部X射线上使用Grad-CAM方法来解释胸部X射线.
K Vanitha1, T R Mahesh2, V Vinoth Kumar3
1Department of Computer Science and Engineering, Faculty of Engineering, Karpagam Academy of Higher Education (Deemed to be University), Coimbatore, India.
BMC medical imaging
|March 25, 2025
概括
这项研究引入了一种新的视觉转换器 (ViT) 模型,具有梯度加权类激活映射 (Grad-CAM),用于通过胸部X射线改进结核病诊断. 人工智能模型实现了高精度,帮助放射科医生进行早期检测.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算病理学计算病理学
背景情况:
- 从胸部X射线进行结核病 (TB) 诊断至关重要,但由于疾病的微妙表现而具有挑战性.
- 使用卷积神经网络 (CNN) 的传统方法需要大量的预处理,缺乏通用性.
- 需要准确和可解释的AI工具来早期检测结核病,特别是在资源有限的环境中.
研究的目的:
- 开发和评估一个新的视觉转换器 (ViT) 模型,与梯度加权类激活映射 (Grad-CAM) 集成,以通过胸部X射线加强结核病诊断.
- 与现有的计算方法相比,提高诊断准确性和模型解释性.
- 为了促进临床应用,并协助放射科医生自动检测结核病.
主要方法:
- 开发了一种视觉变压器 (ViT) 模型,配有Conv2D干和变压器编码器块,可直接处理原始X射线像素.
- 在ViT模型中,自我注意机制被用来捕捉远程依赖和复杂模式.
- 将梯度加权类激活映射 (Grad-CAM) 纳入该模型,以提供对该模型的诊断决策的视觉解释.
主要成果:
- ViT-Grad-CAM模型在验证和测试集上取得了高性能,精度,回忆和F1分数始终高于0.97.
- 该模型在胸部X射线中检测结核病的现有方法相比,显示出更高的性能.
- 格拉德-CAM提供了突出显著区域的视觉热图,增强了模型透明度并帮助放射科医生验证.
结论:
- 新的ViT-Grad-CAM模型在通过胸部X射线自动检测结核病方面取得了显著的改进.
- 该模型的高准确性和可解释性显示出在现实环境中临床应用的巨大潜力.
- 这种方法提高了诊断精度,并支持放射科医生及时准确地诊断结核病.
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