article · Neural Computing and Applications
Leukemia is a malignancy affecting the blood and bone marrow, conventionally diagnosed through labour-intensive and specialised procedures. Detecting blood cancer in paediatric patients demands exceptional precision. To address this, an automated approach named A2M-LEUK combines attention-based machine learning with image processing techniques to detect and classify leukemia cells. Evaluated on a dataset of blood cell images, the algorithm achieved 99.98 percent accuracy, 99.97 percent precision, 100.00 percent recall, and an F1-score of 99.98 percent. These metrics demonstrate high sensitivity and precision in categorising cancerous cells. The system offers an efficient method to support clinical diagnosis and treatment planning for paediatric leukemia, while helping to reduce the overall diagnostic workload faced by medical professionals.
Diagnosing leukemia conventionally requires specialised, time-consuming manual evaluation of blood samples. Automated, highly accurate detection can assist medical staff by swiftly flagging abnormal cells in children. Improving the speed and accuracy of diagnosis helps healthcare providers initiate critical treatments sooner, while easing operational demands on pathology teams.
The algorithm could serve as an automated diagnostic aid within digital pathology platforms and laboratory image analysis software used by medical professionals. Evaluated on a blood cell image dataset, the tool is currently applied and tested in a computational research environment, though the abstract indicates no clinical deployment or integration into commercial diagnostic hardware.
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Abstract Leukemia is a malignancy that affects the blood and bone marrow. Its detection and classification are conventionally done through labor-intensive and specialized methods. The diagnosis of blood cancer in children is a critical task that requires high precision and accuracy. This study proposes a novel approach utilizing attention mechanism-based machine learning in conjunction with image processing techniques for the precise detection and classification of leukemia cells. The proposed attention-augmented algorithm for blood cancer detection in children (A2M-LEUK) is an innovative algorithm that leverages attention mechanisms to improve the detection of blood cancer in children. A2M-LEUK was evaluated on a dataset of blood cell images and achieved remarkable performance metrics: Precision = 99.97%, Recall = 100.00%, F1-score = 99.98%, and Accuracy = 99.98%. These results indicate the high accuracy and sensitivity of the proposed approach in identifying and categorizing leukemia, and its potential to reduce the workload of medical professionals and improve the diagnosis of leukemia. The proposed method provides a promising approach for accurate and efficient detection and classification of leukemia cells, which could potentially improve the diagnosis and treatment of leukemia. Overall, A2M-LEUK improves the diagnosis of leukemia in children and reduces the workload of medical professionals.
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DOI: 10.1007/s00521-023-08678-8
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