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

Background: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.”