An Efficient Encoder-Decoder CNN for Brain Tumor Segmentation in MRI Images

Abstract

An improved Encoder-Decoder Convolutional Neural Network (CNN) architecture is proposed for segmenting brain tumors in Magnetic Resonance Imaging (MRI). It consists of three encoding and decoding blocks. In the first encoding block, each input slice is convolved separately with two different filters and processed into upcoming encoding and decoding blocks for extracting the hierarchy of tumoral features. These are classified using softmax and compared with ground truth for evaluating performance. Experimental results were evaluated based on training and validation images in BRATS-2012, BRATS-2013 and BRATS-2018 datasets, which achieved 46.7%, 30.4% and 5.7% higher dice scores, respectively, compared to the existing segmentation methods.