Issue |
MATEC Web Conf.
Volume 392, 2024
International Conference on Multidisciplinary Research and Sustainable Development (ICMED 2024)
|
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Article Number | 01090 | |
Number of page(s) | 17 | |
DOI | https://doi.org/10.1051/matecconf/202439201090 | |
Published online | 18 March 2024 |
MRI-based brain tumor detection and types of classification using CEHJB-RI and JMST
1 Computer Science and Engineering, KG Reddy College of Engineering & Technology, Hyderabad, India.
2 Information Technology, Stanley College of Engineering and Technology for Women, Hyderabad, India.
3 Computer Science and Engineering, AVN Institute of Engineering & Technology, Hyderabad, India
4 Department of IT, GRIET, Hyderabad, Telangana, India
5 Lovely Professional University, Phagwara, Punjab, India.
The Brain Tumor (BT), which forms in the brain cells and spreads to the whole brain, may lead to death. Hence, early diagnosis of BT is significant. Still, the detection of BT between the skull and brain region is not concentrated, which results in misclassification outcomes. Thus, this article proposes Magnetic Resonance Imaging (MRI)-based BT detection and types’ classification utilizing Carlitz Exponential Hamilton Jacobi Bellman-based Reinforcement Learning (CEHJB-RL) and JenSorensen similarity-based Minimum Spanning Tree (JMST). Primarily, raw MRI images are taken and then pre-processed. Then, with skull and without skull regions are extracted from the pre-processed image and are subjected to the graph construction. Conversely, the edges are detected from the pre-processed image that can be patch-extracted and subjected to graph construction. By utilizing JMST and Morphological Operations (MO), the graphs are constructed. Thereafter, the features are extracted and fed to the classifier. Then, the type of BT is classified by the classifier using CEHJB-RL. Concerning the performance metrics, the outcomes illustrated that the proposed technique attained a higher accuracy (99.27%), which is better than other existing techniques.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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