Enhanced Brain Tumor Classification Through Optimized Semantic Preserved Generative Adversarial Networks.

Abstract

Brain tumor is a most dangerous disease and requires accurate diagnosis in a short period to ensure the best treatment. Traditional methods for brain tumor classification (BTC) are quite effective, even though usually resulting in clinical manual analysis, which takes more time and prone to errors. Initially, the input image is collected from Brain Tumor dataset. The gathered image is given to preprocessing. In preprocessing stage, trust-based distributed set-membership filtering (TDSF) is used to remove the noise. The preprocessed output is fed to the quaternion offset linear canonical transform (QOLCT) for Grayscale statistic and Haralick texture features extraction. Then the extracted features are fed to the Semantic-Preserved Generative Adversarial Network (SPGAN) for classifying the brain tumor into Glioma, Meningioma and Pituitary. Finally, Hunger Games Search Optimization (HGSO) is used to enhance the weight parameters of SPGAN. The proposed BTC-SPGAN-HGSO method attains the accuracies of 99.72% for Glioma, 99.65% for Meningioma, 99.52% for Pituitary and lowest MSE values across all tumor types, with 0.45% for Glioma, 0.39% for Meningioma, and 0.5% for Pituitary, which performs better than existing models. The simulation results highlight the effectiveness of the proposed BTC-SPGAN-HGSO approach in improving the accuracy of BTC and assist neurologists and physicians make exact decisions of diagnostic.