MAEU‐NET: A novel supervised architecture for brain tumor segmentation

Abstract

AbstractA brain tumor is an abnormal growth of cells that damages the neural system and may lead to severe conditions. These cells are irregular in shape and size, and as a result, the manual analysis of these cells from magnetic resonance (MR) images is a cumbersome and time‐consuming task. To alleviate these issues, a novel U‐Net inspired MAEU‐Net (Multiscale context Aggregation and Edge activation U‐Net) is implemented to detect the brain tumor pixels from MR images more precisely. The encoder block of the proposed model is improved by integrating with multiscale feature aggregation and convolution block attention modules (CBAM) enabling the model to extract both pertinent low‐level and high‐level features. Further, the extracted features are subjected to a parallel pooling module to elevate the features that extract the tumor pixels. In addition to this, the output features of the last encoder are fed to a parallel dilated context aggregation module to mitigate the hyper‐parameter size and further elevate the receptive field strength of the proposed model. Apart from this, edge‐enhanced modules are incorporated with skip connections which concatenate the feature maps of the decoder with respective edge‐enhanced encoder feature maps. As a result, this module increases the aspect ratio and reduces blurry edge problems. To illustrate the performance of the proposed MAEU‐Net, it is trained and tested using Kaggle LGG, BraTS 2018, and BraTS 2021 publicly available datasets. The proposed model outperformed the previously existing models by achieving the test loss, dice coefficient, sensitivity, and specificity of 0.0590, 0.901, 0.942, and 0.998, respectively. Simultaneously, the proposed model is quantitatively analyzed by performing ablation and comparative studies and presented that the proposed MAEU‐Net is superior to other previous existing models like U‐Net++, SegNet, etc.