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

作     者：Saini, Hardeep Saini, Davinder Singh 

作者机构：Department of Electronics and Communication Engineering Chandigarh College of Engineering and Technology Chandigarh India 

出 版 物：《Multimedia Tools and Applications》 (Multimedia Tools Appl)

年 卷 期：2024年

页      面：1-25页

核心收录：

学科分类：1004[医学-公共卫生与预防医学(可授医学、理学学位)] 1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 0806[工学-冶金工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Lung cancer 

摘      要：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. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.