Optimizing CNN hyperparameters with genetic algorithms for face mask usage classification

Abstract

Convolutional Neural Networks (CNNs) have gained significant traction in the field of image categorization, particularly in the domains of health and safety. This study aims to categorize the utilization of face masks, which is a vital determinant of respiratory health. Convolutional neural networks (CNNs) possess a high level of complexity, making it crucial to execute hyperparameter adjustment in order to optimize the performance of the model. The conventional approach of trial-and-error hyperparameter configuration often yields suboptimal outcomes and is time-consuming. Genetic Algorithms (GA), an optimization technique grounded in the principles of natural selection, were employed to identify the optimal hyperparameters for Convolutional Neural Networks (CNNs). The objective was to enhance the performance of the model, namely in the classification of photographs into two categories: those with face masks and those without face masks. The convolutional neural network (CNN) model, which was enhanced by the utilization of hyperparameters adjusted by a genetic algorithm (GA), demonstrated a commendable accuracy rate of 94.82% following rigorous testing and validation procedures. The observed outcome exhibited a 2.04% improvement compared to models that employed a trial and error approach for hyperparameter tuning. Our research exhibits exceptional quality in the domain of investigations utilizing Convolutional Neural Networks (CNNs). Our research integrates the resilience of Genetic Algorithms (GA), in contrast to previous studies that employed Convolutional Neural Networks (CNN) or conventional machine learning models without adjusting hyperparameters. This unique approach enhances the accuracy and methodology of hyperparameter tuning in Convolutional Neural Networks (CNNs).