A pneumonia detection method based on the variant structure of broad learning system

LI Keyuan; ZHANG Qinghua; JIN Pengren; XIE Qin

doi:10.16152/j.cnki.xdxbzr.2024-04-009

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A pneumonia detection method based on the variant structure of broad learning system

更新时间：2024-08-01

- A pneumonia detection method based on the variant structure of broad learning system
- Journal of Northwest University (Natural Science Edition) Vol. 54, Issue 4, Pages: 665-676(2024)
- 作者机构：
  
  1.重庆邮电大学　计算智能重庆市重点实验室，重庆　400065
  2.重庆邮电大学　大数据智能计算重点实验室，重庆　400065
  3.重庆邮电大学　旅游多源数据感知与决策技术文化和旅游部重点实验室，重庆　400065
- 作者简介：
- 基金信息：
- DOI：10.16152/j.cnki.xdxbzr.2024-04-009
  CLC： TP391
- Published：25 August 2024，
  
  Received：28 May 2024，
- 稿件说明：
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LI Keyuan, ZHANG Qinghua, JIN Pengren, et al. A pneumonia detection method based on the variant structure of broad learning system. [J]. Journal of Northwest University (Natural Science Edition) 54(4):665-676(2024)
DOI：

LI Keyuan, ZHANG Qinghua, JIN Pengren, et al. A pneumonia detection method based on the variant structure of broad learning system. [J]. Journal of Northwest University (Natural Science Edition) 54(4):665-676(2024) DOI： 10.16152/j.cnki.xdxbzr.2024-04-009.

摘要

肺炎作为常见的呼吸系统疾病，准确、快速地诊断对患者的健康恢复至关重要。随着医疗技术的革新和人工智能的发展，计算机辅助诊断在医学领域的应用日益广泛。深度学习在肺炎检测领域取得了显著的成果，但其庞大的参数数量和复杂的网络结构导致训练时间长、计算资源消耗大等局限性。为了解决上述问题，提出了一种基于宽度学习系统变体结构的肺炎检测方法。该方法在原始宽度学习系统的基础上，引入了级联金字塔结构；同时，利用预训练的EfficientNet网络作为前置特征提取器；此外，还提出了适用于该模型的增量学习算法，包括增加额外的增强节点、特征节点和训练样本，以进一步优化模型性能；最后，在公开的肺炎胸部X射线数据集上进行了对比实验。实验结果表明，该方法实现了92.83%的准确率，AUC值高达98.86%，与众多深度卷积神经网络相比，具有相似的精度，同时大幅缩短了模型的训练时间。

Abstract

Pneumonia as a common respiratory disease

its accurate and rapid diagnosis is crucial to the health of patients. With the innovation of medical technology and the development of artificial intelligence

computer-aided diagnosis has been increasingly used in the medical field. Deep learning has achieved remarkable results in the field of pneumonia detection

but its large number of parameters and complex network structure lead to limitations such as long training time and high consumption of computational resources. To solve the above problems

a pneumonia detection method based on the variant structure of broad learning system is proposed in this paper. The method introduces the cascade pyramid structure on the basis of original broad learning system. Meanwhile

the pre-trained EfficientNet network is utilised as the front feature extractor. In addition

the incremental learning algorithms applicable to the model are proposed in this paper

including adding additional enhancement nodes

feature nodes and training samples to further optimise the model performance. Finally

comparative experiments are conducted on the publicly available dataset of chest X-rays for pneumonia. The experimental results show that the method in this paper achieves 92.83% accuracy and 98.86% AUC value

which are comparable to many deep convolutional neural networks

while the training time of the model is significantly shortened.

关键词

肺炎检测宽度学习系统级联金字塔增量学习

Keywords

pneumonia detectionbroad learning systemcascade pyramidincremental learning

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