一个混合密集卷积网络和模糊推断系统用于肺炎诊断,具有动态症状跟踪
Sulav Baral1, Rabindra Bista1, Sanjog Sigdel1
1Department of Computer Science and Engineering, School of Engineering, Kathmandu University, Dhulikhel, Kavre, Nepal.
PloS one
|October 22, 2025
概括
一个新的混合人工智能 (AI) 系统使用深度学习 (DL) 和模糊逻辑来增强肺炎诊断. 这种人工智能工具提高了准确性和可解释性,特别是在资源有限的环境中.
科学领域:
- 医学成像和诊断 医学成像和诊断
- 医疗保健中的人工智能
- 计算病理学计算病理学
背景情况:
- 肺炎是幼儿和老年人死亡的主要原因.
- 正确的肺炎诊断是具有挑战性的,因为主观的解释和症状的变化.
- 在资源有限的环境中,对放射科医生的有限访问加剧了诊断困难.
研究的目的:
- 开发用于肺炎诊断的混合人工智能系统.
- 整合深度学习 (DL) 的信心评分,DenseNet201,囊网络 (CapsNet) 和模糊推理.
- 提高肺炎检测的诊断准确性,透明度和临床可用性.
主要方法:
- 利用了17,229张胸部X射线图像的数据集,并使用了交叉验证技术.
- 采用了DenseNet201用于特征提取和CapsNet用于空间关系保存.
- 集成的DL信心得分进入一个模糊的推断系统,具有动态的症状调整机制.
主要成果:
- 使用DenseNet201 + CapsNet架构实现了99.01%的测试准确性.
- 证明了强大的概括性,加权精度,回忆和F1得分约为0.987.
- CapsNet的表现优于传统层;模糊的逻辑和动态调整有效地处理了模两可的情况.
结论:
- 混合AI模型为肺炎诊断提供了一个强大的,可解释的决策支持工具.
- 有效地将高性能人工智能与临床决策联系起来.
- 在放射学专业知识有限的环境中,为肺炎诊断提供了显著的好处.
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