Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 27, 20 December 2023
Open
Access | Article
Deep learning-based image classification of MRI brain image
* Author to whom correspondence should be addressed.
Abstract
This article reviews the latest research on MRI brain image classification techniques based on deep learning. Firstly, MRI brain image classification and traditional machine learning methods applied to MRI image classification were briefly introduced. Then, a review was conducted on existing MRI brain image classification methods based on deep learning. The article reviews past and recent related research and provides a detailed introduction to the application of deep learning methods in MRI brain image classification. This includes some traditional machine learning methods and research achievements in deep neural networks (DNN), convolutional neural networks (CNN), and transfer learning. The most commonly used deep learning architecture for image classification is CNN. Research has shown that deep learning methods have high accuracy and performance in MRI brain image classification and can automatically extract image features for effective classification. Therefore, deep learning methods provide doctors with more comprehensive information, help them make more accurate diagnoses and formulate treatment plans, and have broad prospects for application in MRI brain image classification. In the future, the further development of deep learning technology will achieve better auxiliary effects on diagnosis, treatment, and scientific research related to the brain.
Keywords
deep learning, machine learning, MRI brain image, image classification
References
1. Jiang Z, Lyu X, Zhang J, Zhang Q and Wei X 2020 J. Journal of Image and Graphics 25(2) 215-228
2. Song J, Xiao L, Lian Z, Cai Z, Jiang G 2020 J. Journal of Software 32(5) 1427-1460.
3. Liu J, Liu Y, Luo X 2014 J. Application Research of Computers/Jisuanji Yingyong Yanjiu 31 1921-1930
4. Shen D, Wu G, Suk H. 2017 J. Annual review of biomedical engineering 19 221-248
5. Zhan S, Yao Y, Gao H 2013 J. Journal of Electronic Measurement and Instrumentation 27(11) 1067-1072
6. Kumari R 2013 J. Int. J. Eng. Res. Appl 3(4) 1686-1690.
7. Zheng Y, Yang T, Zhou Z 2023 J. Modeling and Simulation 12 1321-1330
8. Mohsen H, El-Dahshan E S A, El-Horbaty E S M, et al 2018 J. Future Computing and Informatics Journal 3(1) 68-71.
9. Ayadi W, Elhamzi W, Charfi I, et al 2021 J. Neural processing letters 53 671-700
10. Fayaz, M. Torokeldiev, N. Turdumamatov, S. Qureshi, M.S. Qureshi, M.B. Gwak 2021 J. Sensors 21(22) 7480
11. Shojaei S, Abadeh M, Momeni Z 2023 J. Expert Systems with Applications 220 119709
12. Mehnatkesh H, Jalali S, Khosravi A, et al 2023 J. Expert Systems with Applications 213 119087
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open Access Instruction).
- Volume Title
- Proceedings of the 2nd International Conference on Modern Medicine and Global Health
- ISBN (Print)
- 978-1-83558-237-4
- ISBN (Online)
- 978-1-83558-238-1
- Published Date
- 20 December 2023
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/27/20240670
- Copyright
- 20 December 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited