Multi-Scale ConvLSTM Attention-Based Brain Tumor Segmentation

In computer vision, there are various machine learning algorithms that have proven to be very effective. Convolutional Neural Networks (CNNs) are a kind of deep learning algorithms that became mostly used in image processing with a remarkable success rate compared to conventional machine learning algorithms. CNNs are widely used in different computer vision fields, especially in the medical domain. In this study, we perform a semantic brain tumor segmentation using a novel deep learning architecture we called multi-scale ConvLSTM Attention Neural Network, that resides in Convolutional Long-Short-Term-Memory (ConvLSTM) and Attention units with the use of multiple feature extraction blocks such as Inception, Squeeze-Excitation and Residual Network block. The use of such blocks separately is known to boost the performance of the model, in our case we show that their combination has also a beneficial effect on the accuracy. Experimental results show that our model performs brain tumor segmentation effectively compared to standard U-Net, Attention U-net and Fully Connected Network (FCN), with 79.78 Dice score using our method compared to 78.61, 73.65 and 72.89 using Attention U-net, standard U-net and FCN respectively.