Brain tumor MRI identification and classification using DWT, PCA and kernel support vector machine

Authors

DOI:

https://doi.org/10.47813/2782-2818-2024-4-1-0133-0152

Keywords:

classification methods, Discrete Wavelet Transform, Feature extraction, Image Segmentation, Pre-Processing, Probabilistic Neural Network, convolution Neural Network

Abstract

Classification, segmentation, and the identification of the infection region in MRI images of brain tumors are labor-intensive and iterative processes. Numerous anatomical structures of the human body may be envisioned using an image processing theory. With basic imaging methods, it is challenging to see the aberrant human brain's structure. The neurological structure of the human brain may be distinguished and made clearer using the magnetic resonance imaging technique. The MRI approach uses a number of imaging techniques to evaluate and record the human brain’s interior features. In this study, we focused on strategies for noise removal, gray-level co-occurrence matrix (GLCM) extraction of features, and segmentation of brain tumor regions based on Discrete Wavelet Transform (DWT) to minimize complexity and enhance performance. In turn, this reduces any noise that could have been left over after segmentation due to morphological filtering. Brain MRI scans were utilized to test the accuracy of the classification and the location of the tumor using probabilistic neural network classifiers. The classifier's accuracy and position detection were tested using MRI brain imaging. The efficiency of the suggested approach is demonstrated by experimental findings, which showed that normal and diseased tissues could be distinguished from one another from brain MRI scans with about 100% accuracy.

Author Biographies

Omar Faruq

Omar Faruq, PhD Research Fellow in Electronic and Electrical Engineering at Bangladesh University of Engineering and Technology, Dhaka, Bangladesh. He is an Assistant Professor of Computer Science and Engineering at BGIFT Institute of Science and Technology, Dhaka, Bangladesh. He received his Post-Graduate degree from the School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, China. He also graduated from Electrical and Electronic Engineering at Daffodil International University, Dhaka, Bangladesh. He engaged in research in Machine Learning, Deep Learning, Artificial Intelligence, Image and Signal Processing, Computer Vision, Free-Space Optical Communication, Optical Fiber Communication, Wireless Communications and Networks, etc.

Md. Jahidul Islam

Islam Md. Jahidul, is A post-graduate student of Software Engineering at Northeastern University, China. He graduated from school of software engineering at Chongqing University of Posts and Telecommunications, China. His interested field of research is computer vision, image processing, signal processing, artificial intelligence, machine learning, deep learning, neural networks, etc.

Md. Sakib Ahmed

Ahmed Md. Sakib, Electrical and Electronic Engineering at Green University Bangladesh, Bangladesh. His interested field of research is image processing, bio-medical, bio-informatics, etc.

Md. Sajib Hossain

Md. Sajib Hossain, Electrical and Electronic Engineering at Green University Bangladesh, Bangladesh. His interested field of research is image processing, bio-medical, bio-informatics, etc.

Narayan Chandra Nath

Nath Narayan Chandra, A post-graduate Master’s program: Control, Microsystems, Microelectronics at University of Bremen, Bremen, Germany. Also received his Post-Graduate degree from School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, China. His interested field of research is Control system, microelectronics, computer vision, image processing, signal processing, artificial intelligence, machine learning, deep learning, neural networks, etc.

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Published

2024-03-28

How to Cite

Faruq, O., Islam , M. J., Ahmed, M. S., Hossain, M. S., & Nath, N. C. (2024). Brain tumor MRI identification and classification using DWT, PCA and kernel support vector machine. Modern Innovations, Systems and Technologies, 4(1), 0133–0152. https://doi.org/10.47813/2782-2818-2024-4-1-0133-0152

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IT and informatics