{"title":"Covid-19胸部x线图像:使用神经网络进行肺分割和诊断","authors":"A. Zhang","doi":"10.5121/ijcsa.2020.10501","DOIUrl":null,"url":null,"abstract":"COVID-19 has caused world-wide disturbances and the machine learning community has been finding ways to combat the disease. Applications of neural networks in image processing tasks allow COVID-19 Chest X-ray images to be meaningfully processed. In this study, the V7 Darwin COVID-19 Chest X-ray Dataset is used to train a U-Net based network that performs lung-region segmentation and a convolutional neural network that performs diagnosis on Chest X-ray images. This dataset is larger than most of the datasets used to develop existing COVID-19 related neural networks. The lung segmentation network achieved an accuracy of 0.9697 on the training set and an accuracy of 0.9575, an Intersectionover-union of 0.8666, and a dice coefficient of 0.9273 on the validation set. The diagnosis network achieved an accuracy of 0.9620 on the training set and an accuracy of 0.9666 and AUC of 0.985 on the validation set.","PeriodicalId":39465,"journal":{"name":"International Journal of Computer Science and Applications","volume":"12 1","pages":"1-11"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Covid-19 Chest X-ray Images: Lung Segmentation and Diagnosis using Neural Networks\",\"authors\":\"A. Zhang\",\"doi\":\"10.5121/ijcsa.2020.10501\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"COVID-19 has caused world-wide disturbances and the machine learning community has been finding ways to combat the disease. Applications of neural networks in image processing tasks allow COVID-19 Chest X-ray images to be meaningfully processed. In this study, the V7 Darwin COVID-19 Chest X-ray Dataset is used to train a U-Net based network that performs lung-region segmentation and a convolutional neural network that performs diagnosis on Chest X-ray images. This dataset is larger than most of the datasets used to develop existing COVID-19 related neural networks. The lung segmentation network achieved an accuracy of 0.9697 on the training set and an accuracy of 0.9575, an Intersectionover-union of 0.8666, and a dice coefficient of 0.9273 on the validation set. The diagnosis network achieved an accuracy of 0.9620 on the training set and an accuracy of 0.9666 and AUC of 0.985 on the validation set.\",\"PeriodicalId\":39465,\"journal\":{\"name\":\"International Journal of Computer Science and Applications\",\"volume\":\"12 1\",\"pages\":\"1-11\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Computer Science and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5121/ijcsa.2020.10501\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Computer Science and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5121/ijcsa.2020.10501","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 3
摘要
COVID-19已经引起了全球范围的动荡,机器学习社区一直在寻找对抗这种疾病的方法。神经网络在图像处理任务中的应用使COVID-19胸部x线图像得到有意义的处理。在本研究中,使用V7 Darwin COVID-19胸部x射线数据集来训练基于U-Net的网络,该网络进行肺区域分割,并对胸部x射线图像进行卷积神经网络诊断。该数据集比用于开发现有COVID-19相关神经网络的大多数数据集都要大。该肺分割网络在训练集上的准确率为0.9697,在验证集上的准确率为0.9575,交集过并度为0.8666,骰子系数为0.9273。该诊断网络在训练集上的准确率为0.9620,在验证集上的准确率为0.9666,AUC为0.985。
Covid-19 Chest X-ray Images: Lung Segmentation and Diagnosis using Neural Networks
COVID-19 has caused world-wide disturbances and the machine learning community has been finding ways to combat the disease. Applications of neural networks in image processing tasks allow COVID-19 Chest X-ray images to be meaningfully processed. In this study, the V7 Darwin COVID-19 Chest X-ray Dataset is used to train a U-Net based network that performs lung-region segmentation and a convolutional neural network that performs diagnosis on Chest X-ray images. This dataset is larger than most of the datasets used to develop existing COVID-19 related neural networks. The lung segmentation network achieved an accuracy of 0.9697 on the training set and an accuracy of 0.9575, an Intersectionover-union of 0.8666, and a dice coefficient of 0.9273 on the validation set. The diagnosis network achieved an accuracy of 0.9620 on the training set and an accuracy of 0.9666 and AUC of 0.985 on the validation set.
期刊介绍:
IJCSA is an international forum for scientists and engineers involved in computer science and its applications to publish high quality and refereed papers. Papers reporting original research and innovative applications from all parts of the world are welcome. Papers for publication in the IJCSA are selected through rigorous peer review to ensure originality, timeliness, relevance, and readability.