Optimal DeepMRSeg based tumor segmentation with GAN for brain tumor classification

The accurate and timely treatment of brain tumor is considered as an imperative part in effectual planning of treatment. However, the manual categorization of tumor in Magnetic resonance imaging (MRI) with same structures or appearance is complex that relies on expertise to discover brain tumor. This paper devises an automatic mechanism that can perform the cataloguing of tumor with MRI. The pre-processing is termed as initial measure to normalize intensity. Here, pre-processing is carried out with min-max normalization. The segmentation is performed with Optimal DeepMRSeg strategy, wherein the DeepMRSeg is trained using newly devised sailfish Political Optimizer (SPO) algorithm. The proposed SPO is devised by combining sailfish optimization algorithm (SOA) and Political Optimizer (PO). Then the Convolutional neural network (CNN) features are extracted and data augmentation is performed. The data augmentation, like random translation, randomized left or right flipping, brightness, rotation or adjustment of contrast is done with CNN. Then, the classification is done with Generative Adversial network (GAN), and trained using Conditional Autoregressive Value at Risk based sailfish political Optimizer (CAViaR-SPO) by combining CAViaR, SOA and PO. The proposed CAViaRSPO-based GAN offered enhanced performance with elevated accuracy of 91.7%, segmentation accuracy of 90%, sensitivity of 92.8%, and specificity of 92.5%.