Innovative brain tumor detection: Stacked random support vector-based hybrid gazelle coati algorithm

Abstract

The process of detecting brain tumors entails capturing brain images, which are then scrutinized to identify any abnormalities. It is crucial to develop and validate medical image classification models in collaboration with healthcare professionals to ensure their safety and effectiveness in clinical settings. These models aid in categorizing the disease based on its type, facilitating appropriate treatment decisions. While traditional methods for brain tumor detection have been useful, they often face limitations in terms of accuracy, scalability, time sensitivity, and cost. To overcome these complexities, a novel Stacked Random Support Vector-based Hybrid Gazelle Coati (SRS-HGC) algorithm is developed to detect brain tumors. This method utilizes feature extraction to capture the shape and size of the tumor. Additionally, Support Vector Machine. Random Forest, and stacked ensemble techniques are employed to classify medical images, into categories such as tumor or non-tumor. In this research, the Hybrid Gazelle optimization and Coati Optimization algorithms are employed for tuning hyperparameter that enhances the efficiency. The analysis are carried out using the Brain Tumor Segmentation (BraTS2020), Br35H, Figshare Brain tumor and REMBRANDT datasets. The results are then compared by demonstrating the efficiency of the SRS-HGC technique in detecting brain tumor diseases.