Deep Learning-Based Leukemia Diagnosis from Bone Marrow Images

Identifying and classifying features in Bone Marrow Aspirate Smear (BMAS) images is essential for diagnosing various leukemias, such as Acute Myeloid Leukemia. The complexity of microscopy image analysis necessitates a computational tool to automate this process, reducing the workload on hematologists. Our study introduces a Deep Learning-based method designed to efficiently detect and classify cell characteristics in BMAS images. Current systems struggle with cell and nucleus segmentation due to variations in cell size, appearance, texture, and overlapping cells, often influenced by different microscopy conditions. We addressed these challenges by experimenting with the Munich AML Morphology Dataset and a custom dataset from Hospital 12 de Octubre in Madrid. The proposed system achieved over 90% accuracy and 92% precision in identifying and classifying leukemia cells, marking a substantial advancement in supporting clinical specialists in their decision-making processes when traditional analysis methods are insufficient.