Benchmarking of Activation Functions for Breast Cancer Detection

Breast cancer is a common disease and one of the leading causes of death globally; there are several methods, technologies, algorithms, or functions to detect their presence. The objective is to develop a benchmarking of activation functions in the detection of breast cancer for its selection with the purpose of increasing the effectiveness in the diagnosis of this disease. The research methodology used in this work is observation in scientific articles, experimental in the implementation of the algorithm, quantitative analysis of the results, and a descriptive approach on the activation functions and the results of the algorithm. The results of this work are an implementation of the Activation Functions Sigmoid, ReLu, Swish, Tanh, and Softmax on the Keras framework; and the realization of benchmarking in Google Colab. It was concluded that this work is an opening towards new knowledge to favor the cooperation and cohesion of different actors; it is a way of betting on knowledge, innovation, and achieving dynamism with planning, analysis, and action of the idea to be implemented for an improvement in the field of health; ReLu has higher accuracy with 98.20% and is the first choice for preparing and training neural networks.