{"title":"A Performance Comparison of Pre-trained Deep Learning Models to Classify Brain Tumor","authors":"A. Diker","doi":"10.1109/EUROCON52738.2021.9535636","DOIUrl":null,"url":null,"abstract":"The brain tumor classification and detection are significant problems in computer-assisted diagnosis (CAD) for medical applications. In this study, the performance comparison of pre-trained deep learning models which are AlexNet, GoogleNet ,and ResNet-18 for the classification of brain MRI images was made. The performances of these models are compared with each other. Experimental results show that the AlexNet model achieves the highest accuracy at 96%. It is followed by the GoogleNet and ResNet-18 model with an accuracy of 90.66% and 88% respectively.","PeriodicalId":328338,"journal":{"name":"IEEE EUROCON 2021 - 19th International Conference on Smart Technologies","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE EUROCON 2021 - 19th International Conference on Smart Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EUROCON52738.2021.9535636","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The brain tumor classification and detection are significant problems in computer-assisted diagnosis (CAD) for medical applications. In this study, the performance comparison of pre-trained deep learning models which are AlexNet, GoogleNet ,and ResNet-18 for the classification of brain MRI images was made. The performances of these models are compared with each other. Experimental results show that the AlexNet model achieves the highest accuracy at 96%. It is followed by the GoogleNet and ResNet-18 model with an accuracy of 90.66% and 88% respectively.