Random CNN structure: tool to increase generalization ability in deep learning

Abstract

The paper presents a novel approach for designing the CNN structure of improved generalization capability in the presence of a small population of learning data. Unlike the classical methods for building CNN, we propose to introduce some randomness in the choice of layers with a different type of nonlinear activation function. The image processing in these layers is performed using either the ReLU or the softplus function. This choice is random. The randomness introduced in the network structure can be interpreted as a special form of regularization. Experiments performed on the detection of images belonging to either melanoma or non-melanoma cases have shown a significant improvement in average quality measures such as accuracy, sensitivity, precision, and area under the ROC curve.