Deep Learning and Improved Particle Swarm Optimization Based Multimodal Brain Tumor Classification
Ayesha Bin T. Tahir, Muhamamd Attique Khan, Majed Alhaisoni, Junaid Ali Khan, Yunyoung Nam, Shuihua Wang, Kashif Javed
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.”