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Efficient Bag-of-Features using Improved Whale Optimization Algorithm for Histopathological Image Classification

机译：使用改进的鲸井优化算法进行组织病理学图像分类的高效特征

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Background: The whale optimization algorithm is one of the popular meta-heuristic algorithmswhich has successfully been applied in various application areas such as image analysis anddata clustering. However, the slow convergence rate and chances of sticking into the local optimadue to improper balance of its exploration and exploitation phases are some of its pitfalls. Therefore,in this paper, a new improved whale optimization algorithm has been proposed. Moreover, the proposedmethod has been used in bag-of-features method for histopathological image classification.Method: The new algorithm, improved whale optimization algorithm, modifies the encircling phaseof original whale optimization algorithm. The proposed algorithm has been used to cluster the extractedfeatures for finding the relevant codewords to be used in the bag-of-features method for histopathologicalimage classification.Results: The efficiency of proposed algorithm has been analyzed on 23 benchmark functions interms of mean fitness, standard deviation values, and convergence behavior. The performance of theimproved whale optimization algorithm based histopathological image classification method hasbeen analyzed on blue histology image dataset and compared with other meta-heuristic based bagof-features methods in terms of recall, precision, F-measure, and accuracy. The experimental resultsvalidate that the proposed method outperforms the considered state-of-the-art methods and attains12% increase in the histopathological image classification accuracy.Conclusion: In this paper, a new improved whale optimization algorithm has been proposed and appliedin bag-of-features method for histopathological image classification. The results of proposedmethod outperform the other existing meta-heuristic methods over standard benchmark functionsand histopathological image dataset.
机译：背景：鲸鱼优化算法是流行的元启发式算法Which成功应用于各种应用领域，例如图像分析andData群集。然而，慢的收敛速度和粘附到当地优选到不当其勘探和剥削阶段的平衡的可能性是其中一些陷阱。因此，在本文中，已经提出了一种新的改进鲸鲸优化算法。此外，预设的方法已被用于组织病理学图像分类的特征袋方法。方法：新算法，改进的鲸瓦优化算法，修改了原始鲸鲸优化算法的环形阶段。所提出的算法已被用于聚类提取的特征，用于找到用于组织病理学视线分类的特征袋方法中使用的相关码字。结果：在23个基准函数互补性，标准的23个基准函数域内进行了分析了所提出的算法的效率偏差值和收敛行为。基于Gly组织图像数据集的基于Graved Whale优化算法基于神经病理学图像分类方法的性能，并与召回，精度，F测量和精度相比，与其他元启发式基于Bagof-Fations方法相比。实验结果过渡，所提出的方法优于所考虑的最先进的方法，并获得组织病理学图像分类精度的增加12％。结论：本文提出了一种新的改进的鲸井优化算法和应用袋 - 组织病理学图像分类的特征方法。 ButoSmethod的结果优于标准基准函数和组织病理学图像数据集的其他现有元启发式方法。

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