{"title":"胸腔图像中的肺炎分类深度学习方法","authors":"Eugenia Arrieta Rodríguez, Agustin Naar, Margarita Gamarra","doi":"10.1088/1757-899x/1299/1/012002","DOIUrl":null,"url":null,"abstract":"\n An algorithm of automatic learning was developed. That is abke to identify radiographic images with a pneumonia and not pneumonia diagnose based on a data set published in the “Kaggle” platform by the Radiological Society of North America, from which we obtained a set of specific images with their labels that were divided by 70% to train a convolutional neuronal network model consisting of two convolutional layers for the extraction of characteristics in each image, and ends with a classification stage in the training of the model, to conclude with 74% in metrics of accuracy given by test tests with 30% of the data set.","PeriodicalId":509593,"journal":{"name":"IOP Conference Series: Materials Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Deep Learning Approach to Classification Pneumonia in Thorax Images\",\"authors\":\"Eugenia Arrieta Rodríguez, Agustin Naar, Margarita Gamarra\",\"doi\":\"10.1088/1757-899x/1299/1/012002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n An algorithm of automatic learning was developed. That is abke to identify radiographic images with a pneumonia and not pneumonia diagnose based on a data set published in the “Kaggle” platform by the Radiological Society of North America, from which we obtained a set of specific images with their labels that were divided by 70% to train a convolutional neuronal network model consisting of two convolutional layers for the extraction of characteristics in each image, and ends with a classification stage in the training of the model, to conclude with 74% in metrics of accuracy given by test tests with 30% of the data set.\",\"PeriodicalId\":509593,\"journal\":{\"name\":\"IOP Conference Series: Materials Science and Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IOP Conference Series: Materials Science and Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1088/1757-899x/1299/1/012002\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IOP Conference Series: Materials Science and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1088/1757-899x/1299/1/012002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Deep Learning Approach to Classification Pneumonia in Thorax Images
An algorithm of automatic learning was developed. That is abke to identify radiographic images with a pneumonia and not pneumonia diagnose based on a data set published in the “Kaggle” platform by the Radiological Society of North America, from which we obtained a set of specific images with their labels that were divided by 70% to train a convolutional neuronal network model consisting of two convolutional layers for the extraction of characteristics in each image, and ends with a classification stage in the training of the model, to conclude with 74% in metrics of accuracy given by test tests with 30% of the data set.