{"title":"人工神经网络在膝关节疾病诊断中的应用","authors":"Konrad Witkowski, Mikołaj Wieczorek","doi":"10.35784/iapgos.5380","DOIUrl":null,"url":null,"abstract":"Following article address the issue of automatic knee disorder diagnose with usage of neural networks. We proposed several hybrid neural net architectures which aim to successfully classify abnormality using MRI (magnetic resonance imaging) images acquired from publicly available dataset. To construct such combinations of models we used pretrained Alexnet, Resnet18 and Resnet34 downloaded from Torchvision. Experiments showed that for certain abnormalities our models can achieve up to 90% accuracy.","PeriodicalId":504633,"journal":{"name":"Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska","volume":"41 3","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"USAGE OF ARTIFICIAL NEURAL NETWORKS IN THE DIAGNOSIS OF KNEE JOINT DISORDERS\",\"authors\":\"Konrad Witkowski, Mikołaj Wieczorek\",\"doi\":\"10.35784/iapgos.5380\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Following article address the issue of automatic knee disorder diagnose with usage of neural networks. We proposed several hybrid neural net architectures which aim to successfully classify abnormality using MRI (magnetic resonance imaging) images acquired from publicly available dataset. To construct such combinations of models we used pretrained Alexnet, Resnet18 and Resnet34 downloaded from Torchvision. Experiments showed that for certain abnormalities our models can achieve up to 90% accuracy.\",\"PeriodicalId\":504633,\"journal\":{\"name\":\"Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska\",\"volume\":\"41 3\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-12-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.35784/iapgos.5380\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.35784/iapgos.5380","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
USAGE OF ARTIFICIAL NEURAL NETWORKS IN THE DIAGNOSIS OF KNEE JOINT DISORDERS
Following article address the issue of automatic knee disorder diagnose with usage of neural networks. We proposed several hybrid neural net architectures which aim to successfully classify abnormality using MRI (magnetic resonance imaging) images acquired from publicly available dataset. To construct such combinations of models we used pretrained Alexnet, Resnet18 and Resnet34 downloaded from Torchvision. Experiments showed that for certain abnormalities our models can achieve up to 90% accuracy.
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