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Multi-objective Genetic Algorithm Based Deep Learning Model for Automated COVID-19 Detection Using Medical Image

S Bansal1, M Singh1, R K Dubey2

  • 1Computer Science and Engineering Department, Indian Institute of Technology Delhi, New Delhi, 110016 India.

Journal of Medical and Biological Engineering
|September 6, 2021
PubMed
Summary
This summary is machine-generated.

A new semi-supervised learning method for COVID-19 detection using CT scans achieves 98.79% accuracy. This rapid, automated tool offers a faster alternative to RT-PCR for screening coronavirus disease.

Keywords:
Convolutional autoencoderCoronavirus (COVID-19)Feature subset selectionMulti-objective genetic algorithm

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Disease Diagnostics

Background:

  • The COVID-19 pandemic, caused by SARS-CoV-2, necessitates rapid diagnostic tools due to high transmission rates.
  • Current RT-PCR tests are precise but time-consuming, highlighting the need for faster screening methods.
  • Chest CT scans offer a potential imaging biomarker for COVID-19 diagnosis.

Purpose of the Study:

  • To develop and evaluate a novel semi-supervised feature learning technique for rapid COVID-19 screening using chest CT scans.
  • To create an automated diagnostic tool that surpasses existing methods in speed and accuracy.
  • To provide a robust binary classification model for distinguishing COVID-19 positive from non-COVID cases.

Main Methods:

  • A three-step architecture was employed: unsupervised feature extraction using a convolutional autoencoder, feature selection via a multi-objective genetic algorithm (MOGA), and binary classification using a Bagging Ensemble of Support Vector Machines (SVM).
  • The model was trained and validated on a dataset comprising 1252 COVID-19 CT scan images from 60 patients.
  • Performance was evaluated on 497 test images.

Main Results:

  • The best-performing model achieved an accuracy of 98.79%, precision of 98.47%, an Area Under the Curve (AUC) of 0.998, and an F1 score of 98.85%.
  • The diagnostic process was highly efficient, with classification completed in approximately 127 milliseconds per image.
  • The proposed method demonstrated superior performance compared to current state-of-the-art COVID-19 diagnostic techniques in terms of both speed and accuracy.

Conclusions:

  • The experimental results validate the superiority of the proposed semi-supervised learning methodology for COVID-19 detection.
  • The study underscores the critical role of feature selection in enhancing the performance of medical image analysis.
  • The developed automated tool offers a promising, rapid, and accurate solution for COVID-19 screening.