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Preserved in Portico This version is not peer-reviewed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)
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. https://doi.org/10.20944/preprints202008.0641.v2 Kim, D. Brain Tumor Detection based on Ensemble Learning. Preprints 2020, 2020080641. https://doi.org/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.
Keywords
brain tumor; machine learning; ensemble methods; convolutional neural networks
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Commenter: Donghyun Kim
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