Enhancing Brain Tumor Classification through Feature Selection with Beetle-Swarm Optimization

The selection of features is a crucial part of machine learning and data mining. The feature sets that are used for classification are always prone to having redundant and correlated features that can affect the performance. The goal of this study is to remove redundant and irrelevant features from the system and retain only relevant ones. This study presents Beetle-Swarm optimization process which involves selecting the features from a segmented image with a Random Forest classifier. The process is performed through a series of steps such as pre-processing, feature extraction, and feature classification. Two objective functions are used to perform the process: image entropy and accuracy function. The proposed method is evaluated on publicly available Kaggle brain tumor dataset. The results of the study revealed that the BSO+RF approach performed well compared to other techniques such as the PSO, ABC, and MVO. The proposed BSO+RF outperforms other similar algorithms in terms of accuracy. It has a performance of 0.8% compared to PSO, while it is slightly better than ABC, and slightly better than MVO. The performance of the proposed BSO+RF algorithm is also higher than that of the comparative techniques, with a learning percentage of 80. It has a low FDR value of less than PSO, ABC, and MVO, which suggests that it has better performance The proposed BSO-RF technique is more accurate than the existing algorithms when it comes to training and testing. In addition, it requires less features to achieve better accuracy. This results in faster computing time and more accuracy. This study presents a new approach to predict cancer using the combination of Beetle Swarm Optimization (BSO) and Random Forest. Beetle-swarm optimization is used to find threshold. This is used to segment the tumor from MR images resulting in better accuracy.