用于 COVID-19 和肺炎分类的优化 Wasserstein 深度卷积生成对抗网络方法

IF 4.9 2区 医学 Q1 ENGINEERING, BIOMEDICAL
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引用次数: 0

摘要

在诊断细菌性和病毒性肺炎以及 COVID-19 等肺部疾病时,样本稀缺往往会导致数据集不平衡,从而难以做出可靠的预测。为解决这一问题,提出了一种用于 COVID-19 和肺炎分类的优化 Wasserstein 深度卷积生成对抗网络技术(CCP WDCGAN-SOA)。所提出的方法利用了两个数据集中的 CT 扫描和 X 光图像:COVID-19 后-前胸部放射影像策展数据集和 COVID QU-Ex 数据集。由于这些数据集存在不平衡,因此引入了标签相关性引导的边界线过采样(LCGBO)方法,以有效平衡类别。数据平衡后,使用多模态层次图协同过滤(MHGCF)对图像进行预处理,以调整大小。随后,将处理后的图像输入使用季节优化算法(SOA)优化的 Wasserstein 深度卷积生成对抗网络(WDCGAN),以提高 COVID-19 和肺炎的分类准确性。在 MATLAB 中的实施表明,CCP-WDCGAN-SOA 技术明显优于现有方法。具体来说,与使用 COVID-19 后胸前放射影像数据集的 DC-CXI-CoviXNet、CPD-CXI-CNN 和 ADC-CXI-DFFC Net 相比,所提出的方法在准确率方面分别提高了 21.5%、23% 和 22.5%,在召回率方面分别提高了 12.3%、17.5% 和 14%,在特异性方面分别提高了 22.3%、27.5% 和 24%。此外,与使用 COVID-QU-Ex 数据集的 ASC-CXI-LRANet、RCP-MIA-CNN 和 AQCD-CR-GAN 相比,拟议方法的准确率提高了 21.52%、27.05% 和 23.24%,召回率提高了 23.71%、26.45% 和 21.74%,特异性提高了 28.61%、22.15% 和 26.44%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Optimized Wasserstein Deep Convolutional Generative Adversarial Network approach for the classification of COVID-19 and pneumonia
In the context of diagnosing lung disorders like bacterial and viral pneumonia and COVID-19, the challenge of sample scarcity often results in imbalanced datasets, making reliable forecasting difficult. To address this, an Optimized Wasserstein Deep Convolutional Generative Adversarial Network Technique was proposed for the Classification of COVID-19 and Pneumonia (CCP WDCGAN-SOA). The proposed approach utilizes CT scan and X-ray images from two datasets: the COVID-19 Posterior-Anterior Chest Radiography Images Curated Dataset and the COVID QU-Ex Dataset. Due to the imbalance in these datasets, a Label Correlation Guided Borderline Oversampling (LCGBO) method was introduced to balance the classes effectively. Following data balancing, the images undergo pre-processing using Multimodal Hierarchical Graph Collaborative Filtering (MHGCF) for resizing. Subsequently, the processed images are fed into a Wasserstein Deep Convolutional Generative Adversarial Network (WDCGAN) optimized with the Seasons Optimization Algorithm (SOA) to enhance classification accuracy for COVID-19 and pneumonia. The implementation in MATLAB demonstrates that the CCP-WDCGAN-SOA technique significantly outperforms existing methods. Specifically, the proposed approach achieves improvements of 21.5 %, 23 %, and 22.5 % in accuracy, 12.3 %, 17.5 %, and 14 % in recall, and 22.3 %, 27.5 %, and 24 % in specificity compared to DC-CXI-CoviXNet, CPD-CXI-CNN, and ADC-CXI-DFFC Net using the COVID-19 Posterior-Anterior Chest Radiography Images Curated Dataset. Additionally, the proposed method shows gains of 21.52%, 27.05%, and 23.24% in accuracy, 23.71%, 26.45%, and 21.74% in recall, and 28.61%, 22.15%, and 26.44% in specificity over ASC-CXI-LRANet, RCP-MIA-CNN, and AQCD-CR-GAN using the COVID-QU-Ex Dataset.
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来源期刊
Biomedical Signal Processing and Control
Biomedical Signal Processing and Control 工程技术-工程:生物医学
CiteScore
9.80
自引率
13.70%
发文量
822
审稿时长
4 months
期刊介绍: Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management. Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.
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