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New brain tumor classification method based on an improved version of whale optimization algorithm

机译:基于改进的鲸鱼优化算法的脑肿瘤分类新方法

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Brain tumor is an abnormal growth of cells in the brain that its diagnosis in the early stages can help us to prevent the dangers of the next stage. In this paper, a new meta-heuristic based methodology is presented for the early diagnosis of the brain tumor to prevent this objection. The proposed method includes three main phases including background removing, feature extraction, and classification based on multilayer perceptron neural network. Here, an improved model of the whale optimization algorithm based on the chaos theory and logistic mapping technique is employed to the optimal selection of the features and the classification stages. The performance analysis of the presented method is compared with some existing methods. Final results showed that based on analyzing CDR, FAR, and FRR as testifying indices, the proposed method has better results than the other similar methods. (C) 2019 Elsevier Ltd. All rights reserved.
机译:脑瘤是大脑细胞的异常生长,早期诊断可以帮助我们预防下一阶段的危险。在本文中,提出了一种新的基于元启发式的方法,用于脑肿瘤的早期诊断以防止这种反对。该方法包括三个主要阶段,包括背景去除,特征提取和基于多层感知器神经网络的分类。在此,基于混沌理论和逻辑映射技术的鲸鱼优化算法改进模型被用于特征和分类阶段的最优选择。将该方法的性能分析与现有方法进行了比较。最终结果表明,在分析CDR,FAR和FRR作为验证指标的基础上,该方法比其他类似方法具有更好的结果。 (C)2019 Elsevier Ltd.保留所有权利。

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