Proceedings of the International Conference on Signal Processing and Computer Vision (SIPCOV-2023)

Brain Tumor Detection Using MRI Images

Authors
Vyankatesh Nyati1, *, Priyanka Pol1, Surabhi Yeltiwar1, Anita Devkar1, Roshani Raut1
1Department of Information Technology, Pimpri Chinchwad College of Engineering, Pune, India
*Corresponding author. Email: vrnyati@gmail.com
Corresponding Author
Vyankatesh Nyati
Available Online 4 October 2024.
DOI
10.2991/978-94-6463-529-4_11How to use a DOI?
Keywords
MRI images; Convolution Neural Network (CNN); Image Classification; Brain Tumor; Artificial Neural Network (ANN)
Abstract

Brain tumors are a severe medical disease that can have serious consequences in terms of morbidity and death. For efficient therapy and improved patient results, early identification of brain tumors is essential. MRI scans allow us to find brain tumors. The doctor will be able to see the abnormal growth on these MRI pictures and determine if the tumor has impacted the brain or not. MRI images can be used to detect typical tissue development and blood clots in the nervous system. The symmetrical and asymmetrical shapes of the brain are examined to identify any abnormalities at the initial stage in the diagnosis of brain tumors. In many of the research papers, machine learning and deep learning algorithms are used to identify brain cancers. The detection of a brain tumor may be carried out much more quickly and precisely when these methods are applied to MRI pictures, which facilitates patient treatment. In the suggested research, a conscious artificial neural network (ANN) and a convolution neural network (CNN) is employed to detect the presence of a brain tumor, and their performance is evaluated. The accuracy of the CNN is almost 95.43%, whereas the accuracy of the ANN model is 91.48%, according to training and testing findings. For the provided dataset, CNN appears to be the most accurate method for identifying brain lesions.

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 Signal Processing and Computer Vision (SIPCOV-2023)
Series
Advances in Engineering Research
Publication Date
4 October 2024
ISBN
978-94-6463-529-4
ISSN
2352-5401
DOI
10.2991/978-94-6463-529-4_11How 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

TY  - CONF
AU  - Vyankatesh Nyati
AU  - Priyanka Pol
AU  - Surabhi Yeltiwar
AU  - Anita Devkar
AU  - Roshani Raut
PY  - 2024
DA  - 2024/10/04
TI  - Brain Tumor Detection Using MRI Images
BT  - Proceedings of the International Conference on Signal Processing and Computer Vision (SIPCOV-2023)
PB  - Atlantis Press
SP  - 108
EP  - 116
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-529-4_11
DO  - 10.2991/978-94-6463-529-4_11
ID  - Nyati2024
ER  -