TY - EJOU AU - Tahir, Ayesha Bin T. AU - Khan, Muhamamd Attique AU - Alhaisoni, Majed AU - Khan, Junaid Ali AU - Nam, Yunyoung AU - Wang, Shui-Hua AU - Javed, Kashif TI - Deep Learning and Improved Particle Swarm Optimization Based Multimodal Brain Tumor Classification T2 - Computers, Materials \& Continua PY - 2021 VL - 68 IS - 1 SN - 1546-2226 AB - 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.” KW - Brain tumor; contrast enhancement; deep learning; feature selection; classification DO - 10.32604/cmc.2021.015154