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.

本文介绍了针对脑磁共振图像（MRI）的自动肿瘤检测和分类方法。这些方法基于混合优化的分类技术，并将脑MRI分为健康，良性或恶性。拟议的系统实现了结合人工神经网络（ANN）的三优化技术。并使用多版本优化器（MVO），蛾-火焰优化器（MFO）和Salp Swarm算法（SSA）进行比较，以研究如何通过选择ANN的最佳参数来成功地利用这些技术来提高分类精度。所提出的技术被应用于哈佛数据库和BRATS挑战数据集，以通过接收器操作特征（ROC）分析来评估性能。这些方法针对几何变换（例如缩放，旋转和扭曲以显示拟议的系统在多大程度上抵抗了这些转换。实验上，与其他已发布的算法相比，所提出的算法实现了最高的分类精度。而且，MVO-ANN算法优于其他提出的算法。