Dilated Capsule Network for Brain Tumor Type Classification Via MRI Segmented Tumor Region Brain tumor recently is considered among the deadliest cancers according to research statistics and have several categories, based on the different characteristics of the tumor. Early detection of the tumor types help to devise treatment plans and achieve high survival rate. Human inspection is noted to be cost effective, error prone and timeconsuming, which have led the interest in Convolutional Neural Networks (CNNs) to automatize the problem. However, CNNs fail to consider the precise location of the features as beneficial, which is harmful, because tumor location and its relationship with the surrounding tissue provide high influence on the brain tumor type. In addition, the CNNs require large amount of dataset for accurate training and prediction. CNNs increasingly reduce image resolution, which result to decrease in classification accuracy. In this work, we incorporated recently developed Capsule Networks (CapsNets) which overcome these drawbacks. The focused contribution is to enhance CapsNets with dilation to maintain the image resolution and improve classification accuracy. We proposed a less trainable CapsNet architecture for brain tumor classification, which takes the segmented tumor regions as inputs within the structure and has the capability of ensuring an increase focus of the CapsNets. While the baseline CapsNets consist of single convolutional layer, our proposed model introduced multiple convolutional layers which achieved an improved performance of 95.54% compared to the related works. Our results indicate that the proposed approach can improve brain tumor classification problem.

An Enhanced UNet for Brain Tumor Segmentation The Convolutional Neural Network (CNN) has been in rapid development these years. It is applied to many fields. Among these, brain tumor segmentation is one of foci. Researchers delves into this with different ways. Some researchers use 3D CNN to handle the brain images for tumor segmentation, and others use 2D CNN modified from models of natural images in semantic segmentation to process separate slices of brain images. Based on properties of brain images and the inherent contradiction of semantic segmentation, we chose 2D UNet as basic model and modified the skip connection in it to propose our model. To verify effect of our model, we introduced our brain image dataset and made experiments on it and BraTS 2017 dataset. Experiments shows that proposed model outperforms the original UNet.