EMU-Net: Automatic Brain Tumor Segmentation and Classification Using Efficient Modified U-Net

Tumor segmentation is a valuable tool for gaining insights into tumors and improving treatment outcomes.Manual segmentation is crucial but time-consuming.Deep learning methods have emerged as key players in automating brain tumor segmentation.In this paper,we propose an efficient modified U-Net architecture,called EMU-Net,which is applied to the BraTS 2020 dataset.Our approach is organized into two distinct phases:classification and segmentation.In this study,our proposed approach encompasses the utilization of the gray-level co-occurrence matrix(GLCM)as the feature extraction algorithm,convolutional neural networks(CNNs)as the classification algorithm,and the chi-square method for feature selection.Through simulation results,the chi-square method for feature selection successfully identifies and selects four GLCM features.By utilizing the modified U-Net architecture,we achieve precise segmentation of tumor images into three distinct regions:the whole tumor(WT),tumor core(TC),and enhanced tumor(ET).The proposed method consists of two important elements:an encoder component responsible for down-sampling and a decoder component responsible for up-sampling.These components are based on a modified U-Net architecture and are connected by a bridge section.Our proposed CNN architecture achieves superior classification accuracy compared to existing methods,reaching up to 99.65%.Additionally,our suggested technique yields impressive Dice scores of 0.8927,0.9405,and 0.8487 for the tumor core,whole tumor,and enhanced tumor,respectively.Ultimately,the method presented demonstrates a higher level of trustworthiness and accuracy compared to existing methods.The promising accuracy of the EMU-Net study encourages further testing and evaluation in terms of extrapolation and generalization.

Deep Q-Learning Based Optimal Query Routing Approach for Unstructured P2P Network

Deep Reinforcement Learning(DRL)is a class of Machine Learning(ML)that combines Deep Learning with Reinforcement Learning and provides a framework by which a system can learn from its previous actions in an environment to select its efforts in the future efficiently.DRL has been used in many application fields,including games,robots,networks,etc.for creating autonomous systems that improve themselves with experience.It is well acknowledged that DRL is well suited to solve optimization problems in distributed systems in general and network routing especially.Therefore,a novel query routing approach called Deep Reinforcement Learning based Route Selection(DRLRS)is proposed for unstructured P2P networks based on a Deep Q-Learning algorithm.The main objective of this approach is to achieve better retrieval effectiveness with reduced searching cost by less number of connected peers,exchangedmessages,and reduced time.The simulation results shows a significantly improve searching a resource with compression to k-Random Walker and Directed BFS.Here,retrieval effectiveness,search cost in terms of connected peers,and average overhead are 1.28,106,149,respectively.