Abstract
This paper presents automatic tumor detection and classification approaches for brain magnetic resonance images (MRI). These approaches are based on hybrid-optimized classification techniques and classify brain MRI to healthy, benign or malignant. The proposed system implements three-optimization techniques combined with Artificial Neural Network (ANN). Multi-Verse Optimizer (MVO), Moth-Flame Optimizer (MFO) and Salp Swarm Algorithm (SSA) are used and compared to examine how these techniques could be successfully employed to enhance the classification accuracy via selecting the optimal parameters of ANN. The proposed techniques are applied to the Harvard database and BRATS challenge dataset to evaluate the performance via Receiver Operation Characteristics (ROC) analysis. The approaches are tested against geometric transformations such as scaling, rotation and warping to show how much the proposed system resists these transformations. Experimentally, the proposed algorithms achieve the highest classification accuracy as compared to the other published ones. Also, the MVO-ANN algorithm outperforms the other proposed algorithms.
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