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Automatic Detection of Covid-19 with Bidirectional LSTM Network Using Deep Features Extracted from Chest X-ray

Kemal Akyol1, Baha Şen2

  • 1Department of Computer Engineering, Faculty of Engineering and Architecture, Kastamonu University, Kastamonu, Turkey. kakyol@kastamonu.edu.tr.

Interdisciplinary Sciences, Computational Life Sciences
|July 27, 2021
PubMed
Summary

A deep learning approach using Bi-LSTM networks effectively detects COVID-19 from chest X-rays with 97.6% accuracy. This method offers a promising automated solution for COVID-19 diagnosis, especially in resource-limited settings.

Keywords:
Artifcial intelligenceBi-LSTMConcatenated deep featuresCovid-19Deep learningX-ray imaging

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Disease Diagnostics

Background:

  • The global COVID-19 pandemic highlighted the need for rapid and accurate diagnostic tools.
  • Traditional diagnostic methods like RT-PCR have limitations, particularly in resource-constrained environments.
  • Computer-aided expert systems are crucial for improving COVID-19 detection efficiency.

Purpose of the Study:

  • To develop and evaluate a deep learning model for detecting COVID-19 and no-finding cases using chest X-ray images.
  • To compare the performance of a Bi-LSTM network with a Deep Neural Network (DNN) for COVID-19 classification.
  • To assess the effectiveness of concatenated deep features versus pre-trained features.

Main Methods:

  • A deep learning approach utilizing chest X-ray images for COVID-19 detection.
  • Classification performance comparison between Bi-LSTM networks and DNNs.
  • Evaluation using fivefold cross-validation and metrics including accuracy, sensitivity, specificity, and precision.

Main Results:

  • The Bi-LSTM network achieved a high accuracy of 97.6%, outperforming the DNN.
  • Concatenated deep features proved more meaningful than individual pre-trained network features.
  • The model demonstrated strong classification performance despite a limited number of COVID-19 images.

Conclusions:

  • The proposed Bi-LSTM network with concatenated deep features is a noteworthy approach for automated COVID-19 monitoring.
  • This deep learning strategy offers a highly sensitive solution for COVID-19 detection.
  • The findings support the utility of AI in enhancing diagnostic capabilities for infectious diseases.