RETRACTED ARTICLE: Cerebrum Tumor Segmentation of High Resolution Magnetic Resonance Images Using 2D-Convolutional Network with Skull Stripping

Abstract

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%.