Deep Learning and Improved Particle Swarm Optimization Based Multimodal Brain Tumor Classification
- Authors
- Tahir, Ayesha Bin T.; Khan, Muhamamd Attique; Alhaisoni, Majed; Khan, Junaid Ali; Nam, Yunyoung; Wang, Shui-Hua; Javed, Kashif
- Issue Date
- 2021
- Publisher
- Tech Science Press
- Keywords
- Brain tumor; contrast enhancement; deep learning; feature selection; classification
- Citation
- Computers, Materials and Continua, v.68, no.1, pp 1099 - 1116
- Pages
- 18
- Journal Title
- Computers, Materials and Continua
- Volume
- 68
- Number
- 1
- Start Page
- 1099
- End Page
- 1116
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/2197
- DOI
- 10.32604/cmc.2021.015154
- ISSN
- 1546-2218
1546-2226
- 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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- Appears in
Collections - College of Engineering > Department of Computer Science and Engineering > 1. Journal Articles
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