A2M-LEUK: attention-augmented algorithm for blood cancer detection in children

Abstract Leukemia is a malignancy that affects the blood and bone marrow. Its detection classification are conventionally done through labor-intensive specialized methods. The diagnosis of cancer in children critical task requires high precision accuracy. This study proposes novel approach utilizing attention mechanism-based machine learning conjunction with image processing techniques for precise leukemia cells. proposed attention-augmented algorithm (A2M-LEUK) an innovative leverages mechanisms to improve children. A2M-LEUK was evaluated on dataset cell images achieved remarkable performance metrics: Precision = 99.97%, Recall 100.00%, F1-score 99.98%, Accuracy 99.98%. These results indicate accuracy sensitivity identifying categorizing leukemia, its potential reduce workload medical professionals leukemia. method provides promising accurate efficient cells, which could potentially treatment Overall, improves reduces professionals.