Vol.68, No.1, 2021, pp.1099-1116, doi:10.32604/cmc.2021.015154
OPEN ACCESS
ARTICLE
Deep Learning and Improved Particle Swarm Optimization Based Multimodal Brain Tumor Classification
  • Ayesha Bin T. Tahir1, Muhamamd Attique Khan1, Majed Alhaisoni2, Junaid Ali Khan1, Yunyoung Nam3,*, Shui-Hua Wang4, Kashif Javed5
1 Department of Computer Science, HITEC University, Taxila, 47040, Pakistan
2 College of Computer Science and Engineering, University of Ha’il, Ha’il, Saudi Arabia
3 Department of Computer Science and Engineering, Soonchunhyang University, Asan, Korea
4 School of Architecture Building and Civil Engineering, Loughborough University, Loughborough, LE11 3TU, UK
5 Department of Robotics, SMME NUST, Islamabad, Pakistan
* Corresponding Author: Yunyoung Nam. Email:
(This article belongs to this Special Issue: AI, IoT, Blockchain Assisted Intelligent Solutions to Medical and Healthcare Systems)
Received 08 November 2020; Accepted 05 February 2021; Issue published 22 March 2021
Abstract
Background: A brain tumor reflects abnormal cell growth. Challenges: Surgery, radiation therapy, and chemotherapy are used to treat brain tumors, but these procedures are painful and costly. Magnetic resonance imaging (MRI) is a non-invasive modality for diagnosing tumors, but scans must be interpretated by an expert radiologist. Methodology: We used deep learning and improved particle swarm optimization (IPSO) to automate brain tumor classification. MRI scan contrast is enhanced by ant colony optimization (ACO); the scans are then used to further train a pretrained deep learning model, via transfer learning (TL), and to extract features from two dense layers. We fused the features of both layers into a single, more informative vector. An IPSO algorithm selected the optimal features, which were classified using a support vector machine. Results: We analyzed high- and low-grade glioma images from the BRATS 2018 dataset; the identification accuracies were 99.9% and 99.3%, respectively. Impact: The accuracy of our method is significantly higher than existing techniques; thus, it will help radiologists to make diagnoses, by providing a “second opinion.”
Keywords
Brain tumor; contrast enhancement; deep learning; feature selection; classification
Cite This Article
A. Bin, M. A. Khan, M. Alhaisoni, J. A. Khan, Y. Nam et al., "Deep learning and improved particle swarm optimization based multimodal brain tumor classification," Computers, Materials & Continua, vol. 68, no.1, pp. 1099–1116, 2021.
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