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Research Article |

Brain Tumor Classification and Identification using PSO and ANFIs

Author(s): Jayashree S. Awati* and Mahesh Kumbhar

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 11, Issue 4

Publisher : FOREX Publication

Published : 20 November 2023

e-ISSN : 2347-470X

Page(s) : 1039-1043

DOI: https://doi.org/10.37391/ijeer.110422

Abstract

Fast Computer-Aided Diagnostic Systems (CAD) have become instrumental in diagnosing diseases. Brain tumors, in particular, pose a significant health challenge. Traditional tumor detection methods relied on radiologists and biopsy, which are time-consuming and detrimental to patients. Early detection is crucial for effective treatment. This system leverages image processing, SWARM intelligence, and Support Vector Machines (SVMs) to detect and classify brain tumors swiftly and accurately. Image processing encompasses preprocessing, segmentation, and feature extraction, with the Particle Swarm Optimization (PSO) method optimizing feature selection. SVMs identify tumor types. While various techniques exist for tumor detection, none achieve 100% accuracy. This system is engineered to provide precise detection.

Keywords: Magnetic Imaging Resonance (MRI), Local Binary Pattern (LBP), Principal Component Analysis (PCA), Otsu’s Segmentation, Support Vector Machine (SVM), Particle Swarm Optimization (PSO).




Jayashree S. Awati*, Electronics and Telecommunication Engineering, Rajarambapu Institute of Technology, Islampur, 415414; Email: jayashree.awati@ritindia.edu

Mahesh Kumbhar, Electronics and Telecommunication Engineering, Rajarambapu Institute of Technology, Islampur, 415414; Email: Mahesh.kumbhar@ritindia.edu

    [1] A. O. Salau, S. Jain, "A survey of feature extraction: types, techniques, and applications," IEEE, 2019.
    [2] L. Brezocnik, "Feature selection for classification using particle swarm optimization," IEEE EUROCON, 2017, pp. 273-276.
    [3] L. Chen, D. Liu, X. Xi, H. Xie, "Image preprocessing algorithm analysis based on Roberts operator and connected region," International Conference on Computer Science and Service System, 2012, pp. 1041-1044.
    [4] J. Ding, J. Wang, X. Kang, X. Hu, "Building an SVM classifier for automated selection of big data," IEEE 6th International Congress on Big Data, 2017, pp. 287-294.
    [5] Swarnima Singh, Vikash Yadav, "An Improved Particle Swarm Optimization for Prediction of Accident Severity," International Journal of Electrical and Electronics Research (IJEER), vol. 9, no. 4, 2021, pp. 42-47.
    [6] J. S. Awati, Prof. S.S. Patil, M.S. Kumbha, "Smart Heart Disease Detection using Particle Swarm Optimization and Support Vector Machine," International Journal of Electrical and Electronics Research (IJEER), vol. 9, no. 4, 2021, pp. 42-47.
    [7] P. Nancy, S. Ravi Kishan, K. P. Rane, K. Kaliyaperumal, M. I. K. Suartama, "Optimized Feature Selection and Image Processing Based Machine Learning Technique for Lung Cancer Detection," International Journal of Electrical and Electronics Research (IJEER), vol. 9, no. 4, 2022, pp. 42-47.

Jayashree S. Awati and Mahesh Kumbhar (2023), Brain Tumor Classification and Identification using PSO and ANFIs. IJEER 11(4), 1039-1043. DOI: 10.37391/ijeer.110422.