Training neural networks to count white blood cells via a minimum counting error objective function

Presents a method for applying neural networks to the bone marrow white blood cell counting problem. The idea is to phrase the objective function in terms of total count error rather than the traditional class-coding approach. A batch-mode training scheme based on backpropagation and gradient descent is derived. The test results show that, although yielding lower classification rates, the network trained to minimize count error performs better in counting than a classification network with the same structure.