Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)

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Enhancing The Efficacy Of Several Deep Learning-Based Models For Identifying Brain Tumours From Magnetic Resonance Images

Authors
T. Priyanka1, *, A. Priyanka1, S. G. Bhavani1, P. S. N. Sarupya1, Y. Balayesu1, K. P. S. Karthik1
1Department of CSE, BVC Engineering College, Odalarevu, A.P, India
*Corresponding author. Email: tpriyanka015@gmail.com
Corresponding Author
T. Priyanka
Available Online 30 July 2024.
DOI
10.2991/978-94-6463-471-6_132How to use a DOI?
Keywords
InceptionResNetV2; DenseNet121; Deep Learning (DL); Magnetic Resonance Imaging (MRI); Artificial Intelligence
Abstract

It is essential to classify brain MRI scans as soon as feasible in order to diagnose brain tumours. There are numerous diagnostic imaging modalities that can be used to identify brain tumours. MRI's high-quality picture production makes it a popular choice for this application. The development of state-of-the-art methods for the automated diagnosis of medical pictures has been greatly influenced by artificial intelligence (AI), more especially by Deep Learning (DL). The aim of this study was to develop an accurate and efficient method for classifying brain tumours using magnetic resonance imaging (MRI). In this work, we leverage well-known deep learning architectures to create a brain tumor diagnosis system. The deep features from brain MRI data are extracted using pre-trained models such as Xception, NasNet Large, DenseNet121, and others.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
Series
Advances in Computer Science Research
Publication Date
30 July 2024
ISBN
978-94-6463-471-6
ISSN
2352-538X
DOI
10.2991/978-94-6463-471-6_132How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - T. Priyanka
AU  - A. Priyanka
AU  - S. G. Bhavani
AU  - P. S. N. Sarupya
AU  - Y. Balayesu
AU  - K. P. S. Karthik
PY  - 2024
DA  - 2024/07/30
TI  - Enhancing The Efficacy Of Several Deep Learning-Based Models For Identifying Brain Tumours From Magnetic Resonance Images
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 1369
EP  - 1376
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_132
DO  - 10.2991/978-94-6463-471-6_132
ID  - Priyanka2024
ER  -