Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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)

How to cite: Kim, D. Brain Tumor Detection: 2 Novel Approaches. Preprints 2020, 2020080641. Kim, D. Brain Tumor Detection: 2 Novel Approaches. Preprints 2020, 2020080641.


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.


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


Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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