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.
Recognition of acute lymphoblastic leukemia cells in microscopic images using k-means clustering and support vector machine classifier
