Comparison of machine learning models for MRI image-based brain tumor classification and segmentation

Yiming Liao

doi:10.54254/2755-2721/19/20231007

Research Article

Open access

Published on 23 October 2023

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Liao,Y. (2023). Comparison of machine learning models for MRI image-based brain tumor classification and segmentation. Applied and Computational Engineering,19,44-49.

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Comparison of machine learning models for MRI image-based brain tumor classification and segmentation

Yiming Liao *^,1,

¹ Zhejiang Chinese Medical University

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/19/20231007

Abstract

Brain tumors have a high-risk factor and are extremely harmful to the human body. With the development of science and technology in recent years, automatic segmentation has become popular in medical diagnosis because it provides higher accuracy than traditional hand segmentation. At present, more and more people start to study and improve it. Due to the non-invasive nature of MRI, MR images are often used to segment and classify brain tumors. However, limited by the inaccuracy and inoperability of manual segmentation, it is very necessary to have a complete and comprehensive automatic brain tumor segmentation and classification algorithm technology. This article discusses the benefits, drawbacks, and areas of application of several traditional algorithms as well as more modern, improved, and more advanced algorithms. Segmentation methods and classification methods can be used to classify these techniques. Convolutional neural networks (CNN), Support vector machines, and Transformers are examples of classification methods. Random forests, decision trees, and improved U-Net algorithms are examples of segmentation methods. To discuss the capability of classification and segmentation, there are three sections in the area used for segmenting brain tumors with three types, including Tumor Core, Enhance Tumor, and Whole Tumor, which could be abbreviated as TC, ET, and WT. Through the comparative analysis of these methods, useful insights for future research are provided.

Keywords

brain tumors, segmentation, classification, MRI

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Cite this article

Liao,Y. (2023). Comparison of machine learning models for MRI image-based brain tumor classification and segmentation. Applied and Computational Engineering,19,44-49.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

Conference website: https://2023.confcds.org/

ISBN：978-1-83558-029-5(Print) / 978-1-83558-030-1(Online)

Conference date: 14 July 2023

Editor：Roman Bauer, Marwan Omar, Alan Wang

Series: Applied and Computational Engineering

Volume number: Vol.19

ISSN：2755-2721(Print) / 2755-273X(Online)

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