Chapter 7 - Comparisons among different stochastic selections of activation layers for convolutional neural networks for health care

Abstract

The classification of biological images is an important task with crucial applications in many fields, such as cell phenotype recognition, detection of cell organelles, and histopathological classification. Bioimage classification can help in early medical diagnosis, allowing automatic disease classification. In this chapter, the authors classify biomedical images using ensembles of neural networks. They create this ensemble using a ResNet50 architecture and employing a set of different activation functions: ReLU, leaky ReLU, Parametric ReLU, Exponential Linear Unit, Adaptive Piecewise Linear Unit, S-Shaped ReLU, Swish, Mish, Mexican Linear Unit, Gaussian Linear Unit, Parametric Deformable Linear Unit, Soft Root Sign, among others.