Brain multi modality image inpainting via deep learning based edge region generative adversarial network

A brain tumor (BT) is considered one of the most crucial and deadly diseases in the world, as it affects the central nervous system and its main functions. Headaches, nausea, and balance problems are caused by tumors pressing on nearby brain tissue and affecting its function. The existing techniques are challenging to analyze diseased brain images since abnormal brain tissues lead to distorted or biased results during image processing, like tissue segmentation and non-rigid registration. To overcome these issues, proposed a DS-GAN model for inpainting brain MRI images. Initially, the input MRI images are segmented using a Gated shape convolution neural network (GS-CNN). In the first GAN, grayscale pixel intensities and the remaining image edges are utilized to create edge generators or edge reconstruction Generative Adversarial Networks (EGAN), which are capable of creating false edges in areas that are missing. The results of the experimental results demonstrated that the Jaccard Index (JI) was 0.82, while the Dice Index (DI) was 0.86. The proposed DS-GAN in terms of L1 loss, PSNR, SSIM, and MSE obtained was 2.18, 0.972, 32.04, and 26.42. As compared to existing techniques, the proposed DS-GAN model achieves an overall accuracy of 99.18%.