RES-KELM fusion model based on non-iterative deterministic learning classifier for classification of Covid19 chest X-ray images

IF 2.1 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Intelligent Systems Pub Date : 2023-01-01 DOI:10.1515/jisys-2022-0235
Arshi Husain, Virendra P. Vishvakarma
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引用次数: 1

Abstract

Abstract In this research, a novel real time approach has been proposed for detection and analysis of Covid19 using chest X-ray images based on a non-iterative deterministic classifier, kernel extreme learning machine (KELM), and a pretrained network ResNet50. The information extraction capability of deep learning and non-iterative deterministic training nature of KELM has been incorporated in the proposed novel fusion model. The binary classification is carried out with a non-iterative deterministic learning based classifier, KELM. Our proposed approach is able to minimize the average testing error up to 2.76 on first dataset, and up to 0.79 on the second one, demonstrating its effectiveness after experimental confirmation. A comparative analysis of the approach with other existing state-of-the-art methods is also presented in this research and the classification performance confirm the advantages and superiority of our novel approach called RES-KELM algorithm.
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基于非迭代确定性学习分类器的RES-KELM融合模型用于新冠肺炎胸片图像分类
本研究提出了一种基于非迭代确定性分类器、核极限学习机(KELM)和预训练网络ResNet50的胸部x线图像实时检测和分析新冠肺炎的新方法。该融合模型结合了深度学习的信息提取能力和KELM的非迭代确定性训练特性。使用基于非迭代确定性学习的分类器KELM进行二元分类。我们提出的方法在第一个数据集上的平均测试误差最小,达到2.76,在第二个数据集上的平均测试误差最小,达到0.79,实验验证了该方法的有效性。本研究还将该方法与其他现有的最先进的方法进行了比较分析,分类性能证实了我们的新方法RES-KELM算法的优势和优越性。
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来源期刊
Journal of Intelligent Systems
Journal of Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
5.90
自引率
3.30%
发文量
77
审稿时长
51 weeks
期刊介绍: The Journal of Intelligent Systems aims to provide research and review papers, as well as Brief Communications at an interdisciplinary level, with the field of intelligent systems providing the focal point. This field includes areas like artificial intelligence, models and computational theories of human cognition, perception and motivation; brain models, artificial neural nets and neural computing. It covers contributions from the social, human and computer sciences to the analysis and application of information technology.
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