Cascading handcrafted features and Convolutional Neural Network for IoTenabled brain tumor segmentation Abstract   The Internet of Things (IoT) has revolutionized the medical world by facilitating data acquisition using various IoT devices. These devices generate the data in multiple forms including text, images, and videos. Given this, the extraction of accurate and useful information from the massive serge IoT generated data is a highly challenging task. Recently, the brain tumor segmentation from IoT generated images has emerged as a promising issue that requires sophisticated and efficient techniques. The accurate brain tumor segmentation is challenging due to large variations in tumor appearance. Existing methods either use handcrafted features based techniques or Convolutional Neural Network (CNN). In this paper, a novel cascading approach for fully automatic brain tumor segmentation has been proposed, which intelligently combines handcrafted features and CNN. First, three handcrafted features are computed namely mean intensity, Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) and then Support Vector Machine (SVM) is employed to perform pixel classification that resulted in Confidence Surface Modality (CSM). This CSM along with the given Magnetic Resonance Imaging (MRI) is fed to novel three pathways CNN architecture. In the experiments on BRATS 2015 dataset, the proposed method achieved promising results with Dice similarity scores of 0.81, 0.76 and 0.73 on complete, core and enhancing tumor, respectively.

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.