Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images

K. Shankar*, Eswaran Perumal, Prayag Tiwari, Mohammad Shorfuzzaman, Deepak Gupta

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

In recent times, COVID-19 infection gets increased exponentially with the existence of a restricted number of rapid testing kits. Several studies have reported the COVID-19 diagnosis model from chest X-ray images. But the diagnosis of COVID-19 patients from chest X-ray images is a tedious process as the bilateral modifications are considered an ill-posed problem. This paper presents a new metaheuristic-based fusion model for COVID-19 diagnosis using chest X-ray images. The proposed model comprises different preprocessing, feature extraction, and classification processes. Initially, the Weiner filtering (WF) technique is used for the preprocessing of images. Then, the fusion-based feature extraction process takes place by the incorporation of gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRM), and local binary patterns (LBP). Afterward, the salp swarm algorithm (SSA) selected the optimal feature subset. Finally, an artificial neural network (ANN) is applied as a classification process to classify infected and healthy patients. The proposed model's performance has been assessed using the Chest X-ray image dataset, and the results are examined under diverse aspects. The obtained results confirmed the presented model's superior performance over the state of art methods.

Original languageEnglish
JournalMULTIMEDIA SYSTEMS
DOIs
Publication statusE-pub ahead of print - 2021
MoE publication typeA1 Journal article-refereed

Keywords

  • Classification
  • COVID-19
  • Feature extraction
  • Fusion model
  • Metaheuristic

Fingerprint

Dive into the research topics of 'Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images'. Together they form a unique fingerprint.

Cite this