Classification of brain lesions on magnetic resonance imaging using superpixel, PSO and convolutional neural network

Abstract

One of the most severe and common brain tumors is gliomas. Manual classification of injuries of this type is a laborious task in the clinical routine. Therefore, this work proposes an automatic method to classify lesions in the brain in 3D MR images based on superpixels, PSO algorithm and convolutional neural network. The proposed method obtained results for the complete, central and active regions, an accuracy of 87.88%, 70.51%, 80.08% and precision of 76%, 84%, 75% for the respective regions. The results demonstrate the difficulty of the network in the classification of the regions found in the lesions.