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- W3156975676 abstract "Three-class brain tumor classification becomes a contemporary research task due to the distinct characteristics of tumors. The existing proposals employ deep neural networks for the three-class classification. However, achieving high accuracy is still an endless challenge in brain image classification. We have proposed a deep dense inception residual network for three-class brain tumor classification. We have customized the output layer of Inception ResNet v2 with a deep dense network and a softmax layer. The deep dense network has improved the classification accuracy of the proposed model. The proposed model has been evaluated using key performance metrics on a publicly available brain tumor image dataset having 3064 images. Our proposed model outperforms the existing model with a mean accuracy of 99.69%. Further, similar performance has been obtained on noisy data." @default.
- W3156975676 created "2021-04-26" @default.
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- W3156975676 date "2021-04-16" @default.
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- W3156975676 title "Three-class brain tumor classification using deep dense inception residual network" @default.
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- W3156975676 doi "https://doi.org/10.1007/s00500-021-05748-8" @default.
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