Brain Tumor identification by Convolution Neural Network with Fuzzy C-mean Model Using MR Brain Images
dc.contributor.author | Isselmou, Abd El Kader | |
dc.contributor.author | Guizhi, Xu | |
dc.contributor.author | Zhang, Shuai | |
dc.contributor.author | Saminu, Sani | |
dc.contributor.author | Javaid, Imran | |
dc.contributor.author | Ahmad, Isah Salim | |
dc.date.accessioned | 2022-01-10T11:18:14Z | |
dc.date.available | 2022-01-10T11:18:14Z | |
dc.date.issued | 2020-12-29 | |
dc.description.abstract | Medical image computing techniques are essential in helping the doctors to support their decision in the diagnosis of the patients. Due to the complexity of the brain structure, we choose to use MR brain images because of their quality and the highest resolution. The objective of this article is to detect brain tumor using convolution neural network with fuzzy c-means model, the advantage of the proposed model is the ability to achieve excellent performance using accuracy, sensitivity, specificity, overall dice and recall values better than the previous models that are already published. In addition, the novel model can identify the brain tumor, using different types of MR images. The proposed model obtained accuracy with 98%. | en_US |
dc.identifier.issn | 1998-4464 | |
dc.identifier.uri | https://uilspace.unilorin.edu.ng/handle/20.500.12484/7301 | |
dc.language.iso | en | en_US |
dc.publisher | North Atlantic University Union | en_US |
dc.subject | MRI | en_US |
dc.subject | CNN | en_US |
dc.subject | FCM | en_US |
dc.subject | Tumor Detection | en_US |
dc.subject | accuracy | en_US |
dc.subject | sensitivity | en_US |
dc.subject | dice | en_US |
dc.title | Brain Tumor identification by Convolution Neural Network with Fuzzy C-mean Model Using MR Brain Images | en_US |
dc.type | Article | en_US |
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