Blood cancer (leukemia) is one of the most serious diseases that affect blood-forming tissues. It usually involves white blood cells. The early detection of this severe disease helps doctors to provide efficient treatment. However, the discovery of this sickness at its first stages is often not easy due to similar morphological characteristics of malignant and healthy blood cells. Flow cytometry was the only used technique for early detection of leukemia, but it is very expensive and usually unavailable in hospitals. Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learning by example. In the last few years, deep learning has achieved great successes to solve concrete problems. In particular, it has proven successful in medical imaging classification. In this work, the authors propose a convolutional neural network (CNN) experiment for the classification of malignant white blood cells from normal ones using a dataset of microscopic images. The proposed approach leads to a balanced model that reaches a high level of accuracy.
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