Optimizing Feature Selection for Brain Tumor Classification using a Hybrid ACCSA and Utilizing Machine Learning Algorithms for classification

This research addresses the challenges associated with brain tumor classification by proposing a novel hybrid approach that combines feature selection and machine learning techniques. A median filter is employed to remove noise from MRI images, followed by feature extraction using the Grey Level Co-Occurrence Matrix (GLCM). To enhance classification accuracy, a hybrid Ant Colony Optimization with Crow Search Optimization Algorithm (HACCSA) is introduced for feature selection, identifying the most relevant features from the extracted set. The selected features are then used to train various machine learning classifiers, including Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN). The classification performance is evaluated using k-fold cross-validation, demonstrating a significant improvement of $\mathbf{9 7 \%}$ compared to existing methods. This research contributes to the advancement of brain tumor classification by providing an effective and efficient approach that leverages feature selection and machine learning techniques.