A Joint Classification Method for COVID-19 Lesions Based on Deep Learning and Radiomics.

IF 2.2 4区医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Tomography Pub Date : 2024-09-05 DOI:10.3390/tomography10090109

Guoxiang Ma, Kai Wang, Ting Zeng, Bin Sun, Liping Yang

{"title":"A Joint Classification Method for COVID-19 Lesions Based on Deep Learning and Radiomics.","authors":"Guoxiang Ma, Kai Wang, Ting Zeng, Bin Sun, Liping Yang","doi":"10.3390/tomography10090109","DOIUrl":null,"url":null,"abstract":"<p><p>Pneumonia caused by novel coronavirus is an acute respiratory infectious disease. Its rapid spread in a short period of time has brought great challenges for global public health. The use of deep learning and radiomics methods can effectively distinguish the subtypes of lung diseases, provide better clinical prognosis accuracy, and assist clinicians, enabling them to adjust the clinical management level in time. The main goal of this study is to verify the performance of deep learning and radiomics methods in the classification of COVID-19 lesions and reveal the image characteristics of COVID-19 lung disease. An MFPN neural network model was proposed to extract the depth features of lesions, and six machine-learning methods were used to compare the classification performance of deep features, key radiomics features and combined features for COVID-19 lung lesions. The results show that in the COVID-19 image classification task, the classification method combining radiomics and deep features can achieve good classification results and has certain clinical application value.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"10 9","pages":"1488-1500"},"PeriodicalIF":2.2000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11435940/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tomography","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3390/tomography10090109","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}

引用次数: 0

Abstract

Pneumonia caused by novel coronavirus is an acute respiratory infectious disease. Its rapid spread in a short period of time has brought great challenges for global public health. The use of deep learning and radiomics methods can effectively distinguish the subtypes of lung diseases, provide better clinical prognosis accuracy, and assist clinicians, enabling them to adjust the clinical management level in time. The main goal of this study is to verify the performance of deep learning and radiomics methods in the classification of COVID-19 lesions and reveal the image characteristics of COVID-19 lung disease. An MFPN neural network model was proposed to extract the depth features of lesions, and six machine-learning methods were used to compare the classification performance of deep features, key radiomics features and combined features for COVID-19 lung lesions. The results show that in the COVID-19 image classification task, the classification method combining radiomics and deep features can achieve good classification results and has certain clinical application value.

查看原文本刊更多论文

基于深度学习和放射组学的 COVID-19 病变联合分类方法。

新型冠状病毒引起的肺炎是一种急性呼吸道传染病。它在短时间内迅速传播，给全球公共卫生带来了巨大挑战。利用深度学习和放射组学方法可以有效区分肺部疾病的亚型，提供更好的临床预后准确性，并辅助临床医生，使其能够及时调整临床管理水平。本研究的主要目的是验证深度学习和放射组学方法在 COVID-19 病变分类中的性能，并揭示 COVID-19 肺病的图像特征。研究提出了一种 MFPN 神经网络模型来提取病变的深度特征，并采用六种机器学习方法比较了深度特征、关键放射组学特征和组合特征对 COVID-19 肺部病变的分类性能。结果表明，在COVID-19图像分类任务中，结合放射组学特征和深度特征的分类方法能取得较好的分类效果，具有一定的临床应用价值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Tomography Medicine-Radiology, Nuclear Medicine and Imaging

CiteScore

2.70

自引率

10.50%

发文量

222

期刊介绍： TomographyTM publishes basic (technical and pre-clinical) and clinical scientific articles which involve the advancement of imaging technologies. Tomography encompasses studies that use single or multiple imaging modalities including for example CT, US, PET, SPECT, MR and hyperpolarization technologies, as well as optical modalities (i.e. bioluminescence, photoacoustic, endomicroscopy, fiber optic imaging and optical computed tomography) in basic sciences, engineering, preclinical and clinical medicine. Tomography also welcomes studies involving exploration and refinement of contrast mechanisms and image-derived metrics within and across modalities toward the development of novel imaging probes for image-based feedback and intervention. The use of imaging in biology and medicine provides unparalleled opportunities to noninvasively interrogate tissues to obtain real-time dynamic and quantitative information required for diagnosis and response to interventions and to follow evolving pathological conditions. As multi-modal studies and the complexities of imaging technologies themselves are ever increasing to provide advanced information to scientists and clinicians. Tomography provides a unique publication venue allowing investigators the opportunity to more precisely communicate integrated findings related to the diverse and heterogeneous features associated with underlying anatomical, physiological, functional, metabolic and molecular genetic activities of normal and diseased tissue. Thus Tomography publishes peer-reviewed articles which involve the broad use of imaging of any tissue and disease type including both preclinical and clinical investigations. In addition, hardware/software along with chemical and molecular probe advances are welcome as they are deemed to significantly contribute towards the long-term goal of improving the overall impact of imaging on scientific and clinical discovery.