Using deep convolutional neural networks with adaptive activation functions for medical CT brain image Classification

recently, imaging has become an essential component in many fields of medical research. Analysis of the diverse medical image types requires sophisticated visualization and processing tools. Deep neural networks have introduced themselves as one of the most important branches of machine learning and have been successfully used in many fields of pattern recognition and medical imaging applications. Among the different networks, convolutional neural networks which are biologically inspired variants of multilayer perceptions are widely used in the medical imaging field. In these networks, activation function plays a significant role especially when the data come in different scales. There is a hope to improve the performance of these networks by using adaptive activation functions which adapts their parameters to the input data. In this paper, we have used a modified version of a successful convolutional neural network tuned for medical image classification and investigated the effect of applying three types of adaptive activation functions on that. These activation functions combine basic activation functions in linear (mixed) and nonlinear (gated and hierarchical) ways. The effectiveness of using these adaptive functions is shown on a CT brain images dataset (as a complex medical dataset). The experiments show that the classification accuracy of the proposed network with adaptive activation functions is higher compared to the ones using basic activation functions.