Brain tumor image pixel segmentation and detection using an aggregation of GAN models with vision transformer

A number of applications in the field of medical analysis require the difficult and crucial tasks of brain tumor detection and segmentation from magnetic resonance imaging (MRI). Given that each type of brain imaging provides distinctive information about the specifics of each tumor component, in order to create a flexible and successful brain tumor segmentation system, we first suggest a normalization preprocessing method along with pixel segmentation. Then creating synthetic images is advantageous in many fields thanks to generative adversarial networks (GANs). In contrast, combining different GANs may enable understanding of the distributed features but it can make the model very complex and confusing. Standalone GAN may only retrieve the localized features in the latent version of an image. To achieve global and local feature extraction in a single model, we have used a vision transformer (ViT) along with a standalone GAN which will further improve the similarity of the images and can increase the performance of the model for detection of tumor. By effectively overcoming the constraint of data scarcity, high computational time, and lower discrimination capability, our suggested model can comprehend better accuracy, and lower computational time and also give the understanding of the information variance in various representations of the original images. The proposed model was evaluated on the BraTS 2020 dataset and Masoud2021 dataset, that is, a combination of the three datasets SARTAJ, Figshare, and BR35H. The obtained results demonstrate that the suggested model is capable of producing fine-quality images with accuracy and sensitivity scores of 0.9765 and 0.977 on the BraTS 2020 dataset as well as 0.9899 and 0.9683 on the Masoud2021 dataset.