A hybrid DenseNet121-UNet model for brain tumor segmentation from MR Images

Abstract

Several techniques are used to detect brain tumors in the medical research field; however, Magnetic Resonance Imaging (MRI) is still the most effective technique used by experts. Recently, researchers have proposed different MRI techniques to detect brain tumors with the possibility of uploading and visualizing the image. In the current decade, deep learning techniques have shown promising results in every research area, especially in bioinformatics and medical image analysis. This paper aims to segment brain tumors using deep learning methods of MR images. The UNet architecture, one of the deep learning networks, is used as a hybrid model with pre-trained DenseNet121 architecture for the segmentation process. During training and testing of the model, we focus on smaller sub-regions of tumors that comprise the complex structure. The proposed model is validated on BRATS 2019 publicly available brain tumor dataset that contains high-grade and low-grade glioma tumors. The experimental results indicate that our model performs better than other state-of-the-art methods presented in this particular area. Specifically, the best Dice Similarity Coefficient (DSC) are obtained by using the proposed approach to segment whole tumor (WT), core tumor (CT), and enhancing tumor (ET).