@Article{cmc.2021.015154,
AUTHOR = {Ayesha Bin T. Tahir, Muhamamd Attique Khan, Majed Alhaisoni, Junaid Ali Khan, Yunyoung Nam, Shui-Hua Wang, Kashif Javed},
TITLE = {Deep Learning and Improved Particle Swarm Optimization Based Multimodal Brain Tumor Classification},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {68},
YEAR = {2021},
NUMBER = {1},
PAGES = {1099--1116},
URL = {http://www.techscience.com/cmc/v68n1/41809},
ISSN = {1546-2226},
ABSTRACT = {<b>Background</b>: 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. <b>Methodology</b>: 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. <b>Results</b>: We analyzed high- and low-grade glioma images from the BRATS 2018 dataset; the identification accuracies were 99.9% and 99.3%, respectively. <b>Impact</b>: The accuracy of our method is significantly higher than existing techniques; thus, it will help radiologists to make diagnoses, by providing a “second opinion.”},
DOI = {10.32604/cmc.2021.015154}
}