{"title":"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","authors":"A.B. Rajendra , B.S. Jayasri , S. Ramya , Shruthi Jagadish","doi":"10.1016/j.bspc.2024.107100","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"100 ","pages":"Article 107100"},"PeriodicalIF":4.9000,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Optimized Wasserstein Deep Convolutional Generative Adversarial Network approach for the classification of COVID-19 and pneumonia\",\"authors\":\"A.B. Rajendra , B.S. Jayasri , S. Ramya , Shruthi Jagadish\",\"doi\":\"10.1016/j.bspc.2024.107100\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":55362,\"journal\":{\"name\":\"Biomedical Signal Processing and Control\",\"volume\":\"100 \",\"pages\":\"Article 107100\"},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-11-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biomedical Signal Processing and Control\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1746809424011583\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical Signal Processing and Control","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1746809424011583","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
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.
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.