Recognition of Pneumonia Image Based on Improved Quantum Neural Network

被引：3
|
作者：
Yumin, Dong [1 ]
Wu, Mingqiu [1 ]
Zhang, Jinlei [1 ]
机构：
[1] Chongqing Normal Univ, Coll Comp & Informat Sci, Chongqing 401331, Peoples R China
来源：
IEEE ACCESS | 2020年 / 8卷 / 08期
基金：
中国国家自然科学基金;
关键词：
Logic gates; Quantum computing; Lung; Diseases; Biological neural networks; Image recognition; Neurons; Quantum gate; pneumonia image recognition; quantum neural network; quantum particle swarm;
D O I：
10.1109/ACCESS.2020.3044697
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The current pneumonia image recognition mainly depends on the doctor's experience, but the CT images of some pneumonia are very similar, which leads to easy misdiagnosis. When using a neural network for pneumonia image recognition, there will be problems such as easy to fall into the local minimum, slow convergence speed and easy to cause oscillation due to the gradient descent method. This paper proposes a quantum BP neural network (QBP) based on quantum particle swarm optimization (QPSO) for pneumonia image recognition. QBP-QPSO realizes efficient training of quantum weights. By comparing the accuracy and speed of the QBP-QPSO and other algorithms for pneumonia CT image recognition, the QBP-QPSO has the advantages of fast convergence speed and high accuracy. Finally, the simulation results of pneumonia image recognition prove the correctness and effectiveness of the method.
引用
收藏
页码：224500 / 224512
页数：13
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