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This survey systematically examines the state\u2010of\u2010the\u2010art in blood cancer detection through image\u2010based analysis, aimed at identifying the most effective computational strategies and highlighting emerging trends. This review focuses on three principal objectives, namely, to categorize and compare traditional machine learning (ML), deep learning (DL), and hybrid learning approaches; to evaluate performance metrics such as accuracy, precision, recall, and area under the ROC curve; and to identify methodological gaps and propose directions for future research. Methodologically, we organize the literature by categorizing the malignancy types\u2014leukemia, lymphoma, and multiple myeloma\u2014and particularizing the preprocessing steps, feature extraction techniques, network architectures, and ensemble strategies employed. 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