Comparison and Combination of Leaky ReLU and ReLU Activation Function and Three Optimizers on Deep CNN for COVID-19 Detection

Abstract

COVID-19 detection is an interesting field of study in the medical world and the commonly used method is classification. In determining the best detection model, several classification architectures, such as SVM, KNN, and CNN were utilized. The CNN is a changeable architecture due to having combinations of varying numbers of hidden layers or different activation and optimizer functions. Therefore, this study uses a deep CNN architecture with a combination of Leaky ReLU activation functions and 3 different optimizers, which include Adagrad, Adadelta, and Adamax. The results showed that the combination of the Leaky ReLU activation function and the Adamax optimizer produced good and stable accuracy in the CRX and CT datasets.