Enhancing classification of lung diseases by optimizing training hyperparameters of the deep learning network

Abstract

The COVID-19 pandemic was triggered by the SARS-CoV-2 virus which caused multiple ill-health conditions in infected individuals. There were many cases that culminated in death. Chest X-ray images became a proven method for spotting thoracic ailments. The resultant availability of huge public datasets of chest X-ray images has great potential in deep learning for lung ailment detection. This paper presents a classification that aims at acquiring the optimal hyperparameters using the metaheuristic algorithm for various pre-trained CNN training processes. The experimental results show that HSAGWO (Hybrid Simulated Annealing Grey Wolf Optimization) outperforms the other contemporary models for optimizing training hyperparameters in the ResNet50 network. The accuracy, precision, sensitivity (recall), specificity, and F1-score values obtained are 98.78%, 98.10%, 99.31%, and 98.64%, respectively, which are significantly better than the values obtained for the existing methods. The objective of this work is to improve classification accuracy and reduce false negatives while keeping computational time to a minimum.