Detection and Classification of Brain Tumor Using Convolution Extreme Gradient Boosting Model and an Enhanced Salp Swarm Optimization

Some types of tumors in people with brain cancer grow so rapidly that their average size doubles in twenty-five days. Precisely determining the type of tumor enables physicians to conduct clinical planning and estimate dosage. However, accurate classification remains a challenging task due to the variable shape, size, and location of the tumors.The major objective of this paper is to detect and classify brain tumors. This paper introduces an effective Convolution Extreme Gradient Boosting model based on enhanced Salp Swarm Optimization (CEXGB-ESSO) for detecting brain tumors, and their types. Initially, the MRI image is fed to bilateral filtering for the purpose of noise removal. Then, the de-noised image is fed to the CEXGB model, where Extreme Gradient Boosting (EXGB) is used, replacing a fully connected layer of CNN to detect and classify brain tumors. It consists of numerous stacked convolutional neural networks (CNN) for efficient automatic learning of features, which avoids overfitting and time-consuming processes. Then, the tumor type is predicted using the EXGB in the last layer, where there is no need to bring the weight values from the fully connected layer. Enhanced Salp Swarm Optimization (ESSO) is utilized to find the optimal hyperparameters of EXGB, which enhance convergence speed and accuracy. Our proposed CEXGB-ESSO model gives high performance in terms of accuracy (99), sensitivity (97.52), precision (98.2), and specificity (97.7).Also, the convergence analysis reveals the efficient optimization process of ESSO, obtaining optimal hyperparameter values around iteration 25. Furthermore, the classification results showcase the CEXGB-ESSO model's capability to accurately detect and classify brain tumors.