<![CDATA[Acute Lymphoblastic Leukemia (ALL) is a hematological malignancy that requires a quick and accurate initial examination. Peripheral Blood Smear (PBS) imaging can be used as a source of cell morphology information, but image-based classification still faces challenges due to variations in the shape, color, and structure of blood cells. This study proposes an ALL multi-class classification model based on CNN–Transformer cross-attention fusion with two image inputs, namely the original PBS image and the segmented image that is already available in the dataset. The main contribution of this study lies in the integration of the local features of the CNN and the global features of the Transformer through the cross-attention mechanism, as well as the evaluation of the model components through baseline comparison and ablation studies. The dataset used consisted of 3,256 pairs of PBS images in four classes, namely Benign, Early, Pre, and Pro. The test results showed that the model obtained an accuracy of 0.9980 and a macro F1-score of 0.9975 on the test data. Nonetheless, this very high performance needs to be interpreted with caution as the research has not involved external validation based on different institutions or direct assessments by pathologists. Therefore, the proposed model is more appropriately positioned as a potential computational approach to the classification of PBS images, rather than as a final clinical diagnostic system. Advanced evaluation of external datasets, patient-based allocation schemes, and expert validation are required to assess the generalization and clinical relevance of the model.]]>
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