Hybrid feedback GMDH-type neural network self-selecting various neurons and its application to medical image diagnosis of lung cancer

Hybrid feedback Group Method of Data Handling (GMDH)-type neural network self-selecting various neurons is applied to the computer aided image diagnosis (CAD) of lung cancer. In this algorithm, three types of neural networks, such as sigmoid function neural network, radial basis function (RBF) neural network and polynomial neural network, can be generated using three types of neuron architectures, and the neural network architecture which is the most fit the complexity of medical images, is selected from these three neural network architectures. Furthermore, this GMDH-type neural network has feedback loop and the structural parameters such as the number of feedback loops, the number of neurons in hidden layers and the relevant input variables are automatically selected so as to minimize the prediction error criterion. This GMDH-type neural network is applied to CAD of lung cancer and two kinds of GMDH-type neural networks which recognize lung regions and lung cancer regions, are automatically generated from the multi-detector row CT (MDCT) images of lungs. It is shown that this GMDH-type neural network is useful for CAD of lung cancer.