A Four-Grade Astrocytoma Classification System with Optimal Feature Selection Using Ant Colony Optimization Algorithm

Abstract

An efficient Four-Grade Astrocytoma brain tumor classification is proposed in this research work. The work highlights the optimal feature selection using a hybrid combination of Ant Colony Optimization (ACO) algorithm and machine learning techniques, leading to better classification accuracy. A diverse set of features including spatial and frequency domain features will provide better discrimination of patterns. ACO provides a robust and efficient mechanism for exploring the vast space of possible feature subsets. A total of 135 features are extracted including Tamura features, Gabor Wavelet Features, Coiflet Wavelet Coefficients, Speeded Up Robust Features, and GLCM features. Ant Colony Optimization is used to find out the optimum set of features. The selected feature set is used for training a Decision Tree classifier, where the Astrocytoma MRI images are categorized into four grades of astrocyoma namely, pilocytic astrocytoma, diffuse astrocytoma, anaplastic astrocytoma and glioblastoma multiforne. Classification is also performed using KNN classifier, SVM classifier and Ensemble Bagged Tree Classifier. Performance metrics like accuracy, sensitivity, specificity, precision and FScore are utilized to evaluate the proposed system’s performance. It is found out that the decision tree classifier outperforms the other classifiers. Thus this integrated approach of ACO with machine learning classifiers results in improved Astrocytoma classification performance aiding clinicians in making accurate treatment decisions.