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...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.展开更多
文摘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.
