Chaotic Flower Pollination with Deep Learning Based COVID-19 Classification Model

T. Gopalakrishnan, Mohamed Yacin Sikkandar, Raed Abdullah Alharbi, P. Selvaraj, Zahraa H. Kareem, Ahmed Alkhayyat, Ali Hashim Abbas

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


The Coronavirus Disease (COVID-19) pandemic has exposed the vulnerabilities of medical services across the globe, especially in underdeveloped nations. In the aftermath of the COVID-19 outbreak, a strong demand exists for developing novel computer-assisted diagnostic tools to execute rapid and cost-effective screenings in locations where many screenings cannot be executed using conventional methods. Medical imaging has become a crucial component in the disease diagnosis process, whereas X-rays and Computed Tomography (CT) scan imaging are employed in a deep network to diagnose the diseases. In general, four steps are followed in image-based diagnostics and disease classification processes by making use of the neural networks, such as network training, feature extraction, model performance testing and optimal feature selection. The current research article devises a Chaotic Flower Pollination Algorithm with a Deep Learning-Driven Fusion (CFPADLDF) approach for detecting and classifying COVID-19. The presented CFPA-DLDF model is developed by integrating two DL models to recognize COVID-19 in medical images. Initially, the proposed CFPA-DLDF technique employs the Gabor Filtering (GF) approach to pre-process the input images. In addition, a weighted voting-based ensemble model is employed for feature extraction, in which both VGG-19 and the MixNet models are included. Finally, the CFPA with Recurrent Neural Network (RNN) model is utilized for classification, showing the work's novelty. A comparative analysis was conducted to demonstrate the enhanced performance of the proposed CFPADLDF model, and the results established the supremacy of the proposed CFPA-DLDF model over recent approaches.

Original languageEnglish
Pages (from-to)6195-6212
Number of pages18
JournalComputers, Materials and Continua
Issue number3
Publication statusPublished - 2023

All Science Journal Classification (ASJC) codes

  • Biomaterials
  • Modelling and Simulation
  • Mechanics of Materials
  • Computer Science Applications
  • Electrical and Electronic Engineering


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