论文标题
使用增强的U-NET模型和经验分析的增强的U-NET模型进行分割
Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysis
论文作者
论文摘要
大脑的癌症是致命的，需要仔细的手术分割。使用卷积神经网络（CNN）使用U-NET对脑肿瘤进行分割。在寻找坏死，水肿，生长和健康组织的重叠时，可能很难从图像中获取相关信息。 2D U-NET网络得到了改进并使用Brats数据集进行了培训，以找到这四个领域。 U-NET可以设置许多编码器和解码器路由，这些路由可用于从可以以不同方式使用的图像中获取信息。为了减少计算时间，我们使用图像分割来排除微不足道的背景细节。 Brats数据集的实验表明，我们提出的用于从MRI（MRI）分割脑肿瘤的模型效果很好。在这项研究中，我们证明了2017、2018、2019和2020的Brats数据集与Brats 2019数据集的骰子分数为0.8717（坏死），0.9506（水肿）和0.9427（增强）。
Cancer of the brain is deadly and requires careful surgical segmentation. The brain tumors were segmented using U-Net using a Convolutional Neural Network (CNN). When looking for overlaps of necrotic, edematous, growing, and healthy tissue, it might be hard to get relevant information from the images. The 2D U-Net network was improved and trained with the BraTS datasets to find these four areas. U-Net can set up many encoder and decoder routes that can be used to get information from images that can be used in different ways. To reduce computational time, we use image segmentation to exclude insignificant background details. Experiments on the BraTS datasets show that our proposed model for segmenting brain tumors from MRI (MRI) works well. In this study, we demonstrate that the BraTS datasets for 2017, 2018, 2019, and 2020 do not significantly differ from the BraTS 2019 dataset's attained dice scores of 0.8717 (necrotic), 0.9506 (edema), and 0.9427 (enhancing).
