Classification of leukemia blood samples using neural networks.

Pattern recognition applied to blood samples for diagnosing leukemia remains an extremely difficult task which frequently leads to misclassification errors due in large part to the inherent problem of data overlap. A novel artificial neural network (ANN) algorithm is proposed for optimizing the classification of multidimensional data, focusing on acute leukemia samples. The programming tool established around the ANN architecture focuses on the classification of normal vs.

Optimizing the classification of acute lymphoblastic leukemia and acute myeloid leukemia samples using artificial neural networks.

Accurate classification of human blood cells plays a decisive role in the diagnosis and treatment of diseases. Artificial Neural Networks (ANN) have been consistently used as a trusted classification tool for this type of analysis. In this study, a new Artificial Neural Network approach is proposed for the multidimensional classification of two of the most common forms of leukemia: Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML), also sometimes called Acute Myelogenous Leukemia.