|Brain tumour segmentation on 3D MRI imaging is one of the most critical deep learning applications. In this paper, for the segmentation of tumour sub-regions in brain MRI images, we study some popular architecture for medical imaging segmentation. We further, inspired by them, proposed an architecture that is an end-to-end trainable, fully convolutional neural network that uses attention block to learn localization of different features of the multiple sub-regions of a tumour. We also experiment with a combination of the weighted cross-entropy loss function and dice loss function on the model's performance and the quality of the output segmented labels. The results of the evaluation of our model are received through BraTS'19 dataset challenge. The model can achieve a dice score of 0.80 for the whole tumour segmentation and dice scores of 0.639 and 0.536 for the other two sub-regions within the tumour on the validation dataset.|
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