Acute lymphoblastic leukemia (ALL) is a serious hematological neoplasis that is characterized by the development of immature and abnormal growth of lymphoblasts. However, microscopic examination of bone marrow is the only way to achieve leukemia detection. Various methods are developed for automatic leukemia detection, but these methods are costly and time-consuming. Hence, an effective leukemia detection approach is designed using the proposed Taylor–monarch butterfly optimization-based support vector machine (Taylor–MBO-based SVM). However, the proposed Taylor–MBO is designed by integrating the Taylor series and MBO, respectively. The sparking process is designed to perform the automatic segmentation of blood smear images by estimating optimal threshold values. By extracting the features, such as texture features, statistical, and grid-based features from the segmented smear image, the performance of classification is increased with less training time. The kernel function of SVM is enabled to perform the leukemia classification such that the proposed Taylor–MBO algorithm accomplishes the training process of SVM. However, the proposed Taylor–MBO-based SVM obtained better performance using the metrics, such as accuracy, sensitivity, and specificity, with 94.5751, 95.526, and 94.570%, respectively.

中文翻译：

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基于 TAYLOR–MONARCH BUTTERFLY 优化的支持向量机用于急性淋巴细胞白血病分类的血液涂片显微图像

急性淋巴细胞白血病 (ALL) 是一种严重的血液肿瘤，其特征是淋巴细胞发育不成熟和异常生长。然而，骨髓显微镜检查是实现白血病检测的唯一途径。开发了各种用于自动检测白血病的方法，但这些方法既昂贵又耗时。因此，使用提出的基于 Taylor-monarch 蝴蝶优化的支持向量机（Taylor-MBO-based SVM）设计了一种有效的白血病检测方法。然而，提出的 Taylor-MBO 是通过分别整合 Taylor 级数和 MBO 设计的。触发过程旨在通过估计最佳阈值来执行血涂片图像的自动分割。通过提取特征，如纹理特征、统计、和基于网格的特征从分割的拖影图像中，分类的性能随着更少的训练时间而提高。支持向量机的核函数能够执行白血病分类，从而提出的泰勒-MBO算法完成了支持向量机的训练过程。然而，提出的基于 Taylor-MBO 的 SVM 使用准确度、灵敏度和特异性等指标获得了更好的性能，分别为 94.5751、95.526 和 94.570%。