Brain Tumor Detection based on Ensemble Learning

Donghyun Kim

doi:10.20944/preprints202008.0641.v2

Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 27 August 2020 / Approved: 28 August 2020 / Online: 28 August 2020 (11:40:15 CEST)
Version 2 : Received: 5 June 2021 / Approved: 8 June 2021 / Online: 8 June 2021 (13:53:28 CEST)

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.

brain tumor; machine learning; ensemble methods; convolutional neural networks

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Comment 1

Received: 8 June 2021
Commenter: Donghyun Kim
Commenter's Conflict of Interests: Author

Comment: Title and descriptions of the project were changed to better reflect the content of the paper. A section of the paper was removed as it detracted from the main content.

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Brain Tumor Detection based on Ensemble Learning

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