Multiclass Brain Tumor Classification using GA-SVM
Abstract—The objective of this study is to develop a CAD system for assisting radiologists in multiclass classification of brain tumors. A new hybrid machine learning system based on the Genetic Algorithm (GA) and Support Vector Machine (SVM) for brain tumor classification is proposed. Texture and intensity features of tumors are taken as input. Genetic algorithm has been used to select the set of most informative input features.< Final Year Project > The study is performed on real 428 post contrast T1-weighted MR images of 55 patients. Primary brain tumors such as Astrocytoma (AS), Glioblastoma Multiforme (GBM), Meningioma (MEN), and child tumor-Medulloblastoma (MED) along with secondary tumor-Metastatic (MET) are classified by GA-SVM classifier. Test results showed that the GA optimization technique has enhanced the overall accuracy of SVM from 56.3 % to 91.7%. Individual class accuracies obtained are: AS-89.8%, GBM-83.3%, MEN-96%, MET-91.8%, MED-97.1%. A comparative study with earlier methods is also done. The study reveals that GA-SVM provides more accurate results than earlier methods and is tested on more diversified dataset.
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