Deep Learning and Improved Particle Swarm Optimization Based Multimodal Brain Tumor Classication

Ayesha Bin T. Tahir*, Muhamamd Attique Khan, Majed Alhaisoni, Junaid Ali Khan, Yunyoung Nam, Shui Hua Wang, Kashif Javed

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

A brain tumor reects 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 classication. 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 classied using a support vector machine. Results: We analyzed high- and low-grade glioma images from the BRATS 2018 dataset; the identication accuracies were 99.9% and 99.3%, respectively. Impact: The accuracy of our method is signicantly higher than existing techniques; thus, it will help radiologists to make diagnoses, by providing a second opinion..

Original languageEnglish
Pages (from-to)1099-1116
Number of pages18
JournalComputers, Materials and Continua
Volume68
Issue number1
DOIs
Publication statusPublished - 22 Mar 2021
Externally publishedYes

Keywords

  • Brain tumor
  • classication
  • contrast enhancement
  • deep learning
  • feature selection

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