Article
Version 1
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Brain Tumor Detection: 2 Novel Approaches
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: 2 Novel Approaches. Preprints 2020, 2020080641. https://doi.org/10.20944/preprints202008.0641.v1 Kim, D. Brain Tumor Detection: 2 Novel Approaches. Preprints 2020, 2020080641. https://doi.org/10.20944/preprints202008.0641.v1
Abstract
In this paper, we propose 2 novel methods for brain tumor detection in MRI images. In the first proposed approach, 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. In the second approach, we expand upon prior studies on convolutional neural networks: a convolutional neural network involving a specific module of layers is used for classification. The first approach achieved accuracy scores of 0.98 and the second approach achieved a score of 0.863, outperforming a benchmark VGG-16 model. Considerations to granular computing and circuit complexity theory 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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