Submitted:
05 June 2021
Posted:
08 June 2021
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Abstract
In this paper, we propose methods for brain tumor detection in MRI images based on ensemble learning. We build upon prior research on ensemble methods by testing the concatenation of pre-trained models: features extracted via transfer learning are merged and segmented by classification algorithms or a stacked ensemble of those algorithms. The proposed approach achieved accuracy scores of 0.98 , outperforming a benchmark VGG-16 model. Considerations to granular computing are given in the paper as well.
Keywords:
brain tumor
; machine learning
; ensemble methods
; convolutional neural networks
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