Optimizing Multi Neural Network Weights for COVID-19 Detection Using Enhanced Artificial Ecosystem Algorithm

Abstract

The role of machine learning in medical research, particularly in addressing the COVID-19 pandemic, has proven to be significant. The current study delineates the design and refinement of an artificial intelligence (AI) framework tailored to differentiate COVID-19 from Pneumonia utilizing X-ray scans in synergy with textual clinical data. The focal point of this research is the amalgamation of diverse neural networks and the exploration of the impact of metaheuristic algorithms on optimizing these networks' weights. The proposed framework uniquely incorporates a lung segmentation process using a pre-trained ResNet34 model, generating a mask for each lung to mitigate the influence of potential extraneous features. The dataset comprised 579 segmented X-ray images (Anteroposterior and Posteroanterior views) of COVID-19 and Pneumonia patients, supplemented with each patient's textual medical data, including age and gender. An enhancement in accuracy from 94.32% to 97.85% was observed with the implementation of weight optimization in the proposed framework. The efficacy of the model in detecting COVID-19 was further ascertained through a comprehensive comparison with various architectures cited in the existing literature.