A lightweight deep model for brain tumor segmentation

Abstract

Brain tumors are one of the major causes of increasing deaths worldwide. It is important to correctly identify cancerous tissues by experts in order to make correct treatment planning and to increase patient survival rates. However, manually tracking and segmentation of cancerous tissues in many sections of volumetric MR data is an error-prone and time-consuming process. Developments in the field of deep learning in recent years allow the tasks performed by humans to be performed with higher accuracy and speeds through the developed automatic systems. In this study, a deep learning-based light-weighted model with 6.78M parameters is proposed for the classification of cancerous tissues in the brain. Cross-validation of the proposed method on a public data set results in 84.61%, 82.54%, and 87.15% Boundary F1, mean IoU, and mean accuracy, respectively, shows the robustness of the proposed model.