Multi-COVID-Net: Multi-objective optimized network for COVID-19 diagnosis from chest X-ray images

Coronavirus Disease 2019 (COVID-19) had already spread worldwide, and healthcare services have become limited in many countries. Efficient screening of hospitalized individuals is vital in the struggle toward COVID-19 through chest radiography, which is one of the important assessment strategies. This allows researchers to understand medical information in terms of chest X-ray (CXR) images and evaluate relevant irregularities, which may result in a fully automated identification of the disease. Due to the rapid growth of cases every day, a relatively small number of COVID-19 testing kits are readily accessible in health care facilities. Thus it is imperative to define a fully automated detection method as an instant alternate treatment possibility to limit the occurrence of COVID-19 among individuals. In this paper, a two-step Deep learning (DL) architecture has been proposed for COVID-19 diagnosis using CXR. The proposed DL architecture consists of two stages, feature extraction and classification. The Multi-Objective Grasshopper Optimization Algorithm (MOGOA) is presented to optimize the DL network layers; hence, these networks have named as Multi-COVID-Net. This model classifies the Non-COVID-19, COVID-19, and pneumonia patient images automatically. The Multi-COVID-Net has been tested by utilizing the publicly available datasets, and this model provides the best performance results than other state-of-the-art methods. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

Coronavirus Disease 2019 (COVID-19) has spread worldwide, leading to limited healthcare services in many countries. Screening hospitalized individuals efficiently through chest radiography is crucial in the fight against COVID-19. A two-step Deep Learning (DL) architecture, named Multi-COVID-Net, has been proposed for COVID-19 diagnosis using chest X-ray images, which outperforms other state-of-the-art methods in terms of performance results when tested with publicly available datasets.