S. Tripathi, Taresh Sarvesh Sharan, Shiru Sharma, N. Sharma
{"title":"基于改进深度学习网络的MR图像脑肿瘤分割","authors":"S. Tripathi, Taresh Sarvesh Sharan, Shiru Sharma, N. Sharma","doi":"10.1109/ICSCC51209.2021.9528298","DOIUrl":null,"url":null,"abstract":"This paper presents a modified segmentation network for brain tumour segmentation in Magnetic Resonance Images. The early detection of brain tumour is quite mandatory for planning the treatment. This work proposes a computer-based automatic approach for the segmentation of brain tumour. The network proposed in this paper effectively delineated the boundaries of the brain tumour region. Exceedingly good results were obtained when the trained network was fed with other datasets. The network also showed a good improvement in the results when it was tested on real-time MRI datasets. An improvement of 7.6% and 7% was observed in the mIoU and BF score when the real time MR dataset of brain tumour was applied to the network. The network was incorporated using depthwise separable convolution.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"138 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Segmentation of Brain Tumour in MR Images Using Modified Deep Learning Network\",\"authors\":\"S. Tripathi, Taresh Sarvesh Sharan, Shiru Sharma, N. Sharma\",\"doi\":\"10.1109/ICSCC51209.2021.9528298\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a modified segmentation network for brain tumour segmentation in Magnetic Resonance Images. The early detection of brain tumour is quite mandatory for planning the treatment. This work proposes a computer-based automatic approach for the segmentation of brain tumour. The network proposed in this paper effectively delineated the boundaries of the brain tumour region. Exceedingly good results were obtained when the trained network was fed with other datasets. The network also showed a good improvement in the results when it was tested on real-time MRI datasets. An improvement of 7.6% and 7% was observed in the mIoU and BF score when the real time MR dataset of brain tumour was applied to the network. The network was incorporated using depthwise separable convolution.\",\"PeriodicalId\":382982,\"journal\":{\"name\":\"2021 8th International Conference on Smart Computing and Communications (ICSCC)\",\"volume\":\"138 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 8th International Conference on Smart Computing and Communications (ICSCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSCC51209.2021.9528298\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSCC51209.2021.9528298","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Segmentation of Brain Tumour in MR Images Using Modified Deep Learning Network
This paper presents a modified segmentation network for brain tumour segmentation in Magnetic Resonance Images. The early detection of brain tumour is quite mandatory for planning the treatment. This work proposes a computer-based automatic approach for the segmentation of brain tumour. The network proposed in this paper effectively delineated the boundaries of the brain tumour region. Exceedingly good results were obtained when the trained network was fed with other datasets. The network also showed a good improvement in the results when it was tested on real-time MRI datasets. An improvement of 7.6% and 7% was observed in the mIoU and BF score when the real time MR dataset of brain tumour was applied to the network. The network was incorporated using depthwise separable convolution.