The automatic segmentation of the tumor region from Magnetic Resonance cerebrum imageries is a difficult task in medical image analysis. Numerous techniques have been created with the goal of improving the segmentation effectiveness of the automated framework. As of late, Convolutional Neural Networks have accomplished better performance in various recognition tasks. In this paper, 2D-ConvNet with skull stripping (SS-2D ConvNet) based brain tumor segmentation technique have been presented. In the proposed method, initially, the input MRI images are preprocessed to reduce noise and skull stripped to correct the contrast and non-uniformity. It is further processed through the 2D-ConvNet for the segmentation of brain tumor. In particular, the proposed method has been compared with other existing methods, and it achieves better performances and yield precise segmentation with dice scores of 91%, accuracy of 89%, specificity of 98%, and sensitivity of 87%.

在医学图像分析中，从磁共振大脑图像中自动分割肿瘤区域是一项艰巨的任务。为了提高自动化框架的分割效果，已经创建了许多技术。最近，卷积神经网络在各种识别任务中都取得了更好的性能。本文提出了基于颅骨剥离的二维ConvNet（SS-2D ConvNet）脑肿瘤分割技术。在提出的方法中，首先，对输入的MRI图像进行预处理以减少噪声，并去除颅骨以校正对比度和不均匀性。它通过2D-ConvNet进行进一步处理以分割脑肿瘤。特别是，已将提出的方法与其他现有方法进行了比较，