Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


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

Series Vol. 17 , 23 October 2023


Open Access | Article

The application and challenges of artificial intelligence in brain tumor recognition

Zhihao Han * 1
1 Macquarie University

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 17, 17-22
Published 23 October 2023. © 2023 The Author(s). Published by EWA Publishing
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.
Citation Zhihao Han. The application and challenges of artificial intelligence in brain tumor recognition. ACE (2023) Vol. 17: 17-22. DOI: 10.54254/2755-2721/17/20230904.

Abstract

There is no denying the fact that Artificial intelligence technology has developed rapidly in recent years. In medicine, especially in brain tumor detection field, it has attracted great attention. By combining artificial intelligence with physiological imaging, the classification of brain tumors and the selection of the best treatment options can become more accurate and precise. AI brain tumor detection can reduce the rate of misdiagnosis and improve the speed of diagnosis. The article researches the method of AI in brain tumor detection. The process of the general method can be divided into four phases: Data collection, Preprocessing, Feature extraction, and Classification. At the same time, this article also analyzes the application of AI in brain tumor detection and treatment, including the advantages and challenges of AI application, development direction, and conclusions. As a review paper, this paper provides a relatively complete overview of this field. Even though the growing presence of AI technology in the brain tumor medical field is already bringing greater assistance, there is still a lot of room to improve. Indeed, a highly accurate, explainable system is needed in the future. With the rapid development of AI methods, so do the corresponding high-performance hardware become increasingly essential.

Keywords

brain tumor detection, artificial intelligence, machine learning

References

1. Ayadi W et al 2021 Deep CNN for brain tumor classification Neural Processing Letters 53 pp 671-700

2. Amin J et al 2021 Brain tumor detection and classification using machine learning: a comprehensive survey Complex & Intelligent Systems pp 1-23

3. Jian A Sidong L and Antonio D I 2022 Artificial intelligence for survival prediction in brain tumors on neuroimaging Neurosurgery 91.1 pp 8-26

4. Philip A K et al 2023 Artificial Intelligence and Precision Medicine: A New Frontier for the Treatment of Brain Tumors Life 13.1 24

5. Deepak S and Ameer P M 2019 Brain tumor classification using deep CNN features via transfer learning Computers in biology and medicine 111 103345.

6. Segato A et al 2020 Artificial intelligence for brain diseases: A systematic review APL bioengineering 4(4) 041503

7. King M R 2023 The future of AI in medicine: A perspective from a chatbot Annals of Biomedical Engineering 51(2) pp 291-295

8. A K K S et al 2022 A hybrid deep CNN-Cov-19-Res-Net Transfer learning architype for an enhanced Brain tumor Detection and Classification scheme in medical image processing Biomedical Signal Processing and Control 76

9. Abdalla H E M et al 2018 Brain Tumor Detection by using Artificial Neural Network," 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), Khartoum, Sudan pp 1-6

10. Palmisciano P 2020 Attitudes of patients and their relatives toward artificial intelligence in neurosurgery World neurosurgery 138 pp e627-e633

11. Abd-Ellah M K et al 2019 A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned. Magnetic resonance imaging 61 pp 300-318

12. Yu Q et al 2019 Semantic segmentation of intracranial hemorrhages in head CT scans 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS). IEEE pp 112-115

13. Srinivas C et al 2022 Deep transfer learning approaches in performance analysis of brain tumor classification using MRI images Journal of Healthcare Engineering

14. Woźniak M 2021 Deep neural network correlation learning mechanism for CT brain tumor detection Neural Computing and Applications pp 1-16

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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Volume Title
Proceedings of the 5th International Conference on Computing and Data Science
ISBN (Print)
978-1-83558-025-7
ISBN (Online)
978-1-83558-026-4
Published Date
23 October 2023
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/17/20230904
Copyright
© 2023 The Author(s)
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

Copyright © 2023 EWA Publishing. Unless Otherwise Stated