Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

Brain Tumor Detection based on Ensemble Learning

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)

How to cite: Kim, D. Brain Tumor Detection based on Ensemble Learning. Preprints 2020, 2020080641 (doi: 10.20944/preprints202008.0641.v2). Kim, D. Brain Tumor Detection based on Ensemble Learning. Preprints 2020, 2020080641 (doi: 10.20944/preprints202008.0641.v2).

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.

Subject Areas

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

Comments (1)

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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