Enhancement of CT Brain Images Classification Based on Deep Learning Network with Adaptive Activation Functions

Abstract

Deep neural networks are one of the most important branches of machine learning that have been recently used in many fields of pattern recognition and machine vision applications successfully. One of the most famous networks in this area is convolutional neural networks which are biologically inspired variants of multi-layer perceptions. In these networks, activation function plays a significant role especially when the data come in different scales. Recently, there is an interest to adaptive activation functions which adapts their parameters to the input data during network training process. Therefore, in this paper, inspired from a successful convolutional neural network tuned for medical image classification, we have investigated the effect of applying adaptive activation functions in a modified convolutional network by combining basic activation functions in linear (mixed) and nonlinear (gated) ways. The effectiveness of using these adaptive functions is shown on a CT brain images dataset (as a complex medical dataset) and the well-known MNIST hand-written digits dataset. The done experiments show that the classification accuracy of the proposed network with adaptive activation functions is higher compared to the ones using basic activation functions.