Applications of Deep Neural Networks with Fractal Structure and Attention Blocks for 2D and 3D Brain Tumor Segmentation.

ABSTRACT: In this paper, we propose a novel deep neural network (DNN) architecture with fractal structure and attention blocks. The new method is tested to identify and segment 2D and 3D brain tumor masks in normal and pathological neuroimaging data. To circumvent the problem of limited 3D volumetric datasets with raw and ground truth tumor masks, we utilized data augmentation using affine transformations to significantly expand the training data prior to estimating the network model parameters. The proposed Attention-based Fractal Unet (AFUnet) technique combines benefits of fractal convolutional networks, attention blocks, and the encoder-decoder structure of Unet. The AFUnet models are fit on training data and their performance is assessed on independent validation and testing datasets. Th

SUBMITTER: Cheng K 

PROVIDER: S-EPMC11671157 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature