{"title":"Multi-Classification of brain tumor based on deep CNN","authors":"Wadhah Ayadi, W. Elhamzi, M. Atri","doi":"10.1109/SETIT54465.2022.9875468","DOIUrl":null,"url":null,"abstract":"In the last decades, brain tumors are considered one of the mortal cancers in the world. The right tumors detection and identification in the early phases have a significant role to select an accurate treatment. Due to the increasing number of patients and brain tumor types, the manual analyses of Magnetic Resonance Imaging (MRI) images represent a tiring routine and can lead to human errors. In the goal to surpass these problems, an automatic CAD system is needed. We discussed, in this paper, a new model to classify brain tumors using CNN. The suggested scheme is experimentally evaluated on a public dataset. The proposed approach yields a convincing performance compared to previous techniques based on the experimental results.","PeriodicalId":126155,"journal":{"name":"2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SETIT54465.2022.9875468","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In the last decades, brain tumors are considered one of the mortal cancers in the world. The right tumors detection and identification in the early phases have a significant role to select an accurate treatment. Due to the increasing number of patients and brain tumor types, the manual analyses of Magnetic Resonance Imaging (MRI) images represent a tiring routine and can lead to human errors. In the goal to surpass these problems, an automatic CAD system is needed. We discussed, in this paper, a new model to classify brain tumors using CNN. The suggested scheme is experimentally evaluated on a public dataset. The proposed approach yields a convincing performance compared to previous techniques based on the experimental results.