<![CDATA[Leukemia is a type of blood cancer characterized by the uncontrolled proliferation of abnormal white blood cells that originate from the bone marrow. Early detection of leukemia poses a significant challenge in the medical field, as the conventional diagnostic process still relies on manual microscopic observation by hematologists, which is time-consuming and prone to subjective errors. This study aims to analyze the potential of the Support Vector Machine (SVM) algorithm in optimizing the classification of leukemia cell images based on morphological and texture features extracted from microscopic images. The test results show that the SVM model with the RBF kernel provides the best performance with an accuracy of 96.4%, a precision of 95.8%, a recall of 96.1%, and an F1-score of 96.0%, surpassing the results of linear and polynomial kernels. The analysis shows that the use of a combination of shape and texture features has a significant effect on improving classification accuracy.]]>
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