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

Tripti Goel, R. Murugan, Seyedali Mirjalili, Deba Kumar Chakrabartty

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)


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.

Original languageEnglish
Article number108250
JournalApplied Soft Computing
Publication statusPublished - Jan 2022


  • Chest X-ray images
  • CNN
  • COVID-19
  • Deep learning
  • Multi-objective optimization


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