A bi-stage feature selection approach for COVID-19 prediction using chest CT images

Shibaprasad Sen, Soumyajit Saha, Somnath Chatterjee, Seyedali Mirjalili, Ram Sarkar

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

The rapid spread of coronavirus disease has become an example of the worst disruptive disasters of the century around the globe. To fight against the spread of this virus, clinical image analysis of chest CT (computed tomography) images can play an important role for an accurate diagnostic. In the present work, a bi-modular hybrid model is proposed to detect COVID-19 from the chest CT images. In the first module, we have used a Convolutional Neural Network (CNN) architecture to extract features from the chest CT images. In the second module, we have used a bi-stage feature selection (FS) approach to find out the most relevant features for the prediction of COVID and non-COVID cases from the chest CT images. At the first stage of FS, we have applied a guided FS methodology by employing two filter methods: Mutual Information (MI) and Relief-F, for the initial screening of the features obtained from the CNN model. In the second stage, Dragonfly algorithm (DA) has been used for the further selection of most relevant features. The final feature set has been used for the classification of the COVID-19 and non-COVID chest CT images using the Support Vector Machine (SVM) classifier. The proposed model has been tested on two open-access datasets: SARS-CoV-2 CT images and COVID-CT datasets and the model shows substantial prediction rates of 98.39% and 90.0% on the said datasets respectively. The proposed model has been compared with a few past works for the prediction of COVID-19 cases. The supporting codes are uploaded in the Github link: https://github.com/Soumyajit-Saha/A-Bi-Stage-Feature-Selection-on-Covid-19-Dataset

Original languageEnglish
JournalApplied Intelligence
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Chest CT image
  • Convolutional neural network
  • Coronavirus
  • COVID-19 dataset
  • Deep learning
  • Dragonfly algorithm
  • Feature selection

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