通过使用胸部X射线数据集的混合ZFNet-量子神经网络对肺炎进行分类
Tayyaba Shahwar1, Fatma Mallek2, Ateeq Ur Rehman3
1Department of Electrical Engineering, Superior University, Lahore 54000, Pakistan.
Current medical imaging
|August 23, 2024
概括
这项研究引入了一种新的量子深度神经网络 (QDNN) 用于X射线检测肺炎,达到96.5%的准确性. 混合模型将经典的深度学习与量子算法集成在一起,以增强诊断能力.
科学领域:
- 量子计算是一种量子计算.
- 人工智能的人工智能
- 医学成像分析 医学成像分析
背景情况:
- 深度神经网络 (DNN) 在从X射线诊断肺炎方面表现有前途.
- 量子深度神经网络 (QDNNs) 为进一步的诊断增强提供了潜力.
- 将量子算法与神经网络集成,可以改善肺炎检测.
研究的目的:
- 引入一种混合技术,即ZFNet-量子神经网络,用于检测肺炎.
- 为了利用量子计算原理进行增强的特征提取和分类.
- 评估拟议的QDNN模型与传统深度学习方法的性能.
主要方法:
- 开发了一种混合模型,将ZFNet (深度转移学习模型) 与量子算法结合起来.
- 显著的特征被ZFNet提取出来,并使用量子设备上的参数化量子电路进行处理.
- 量子电路利用量子比特,叠加和纠来从4098个提取的特征中生成4个特征.
主要成果:
- 量子神经网络ZFNet在检测肺炎方面取得了96.5%的准确性.
- 这种混合模型的性能优于标准的深度转移学习网络 (CNN),其准确率达到94%.
- 该模型利用了Adam优化器和一个带有量子门的六层量子电路.
结论:
- 集成的ZFNet-量子学习网络在肺炎检测方面的传统深度学习方面表现出卓越的性能.
- 这种混合经典-量子方法为肺炎诊断提供了一种高效和自动化的方法.
- 该技术有可能显著提高医疗保健诊断网络的速度和准确性.
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