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 Nam^3,*, 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 the Special Issue: AI, IoT, Blockchain Assisted Intelligent Solutions to Medical and Healthcare Systems)

Computers, Materials & Continua 2021, 68(1), 1099-1116. https://doi.org/10.32604/cmc.2021.015154

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.”

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