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X-ray image based COVID-19 detection using evolutionary deep learning approach.

Seyed Mohammad Jafar Jalali1, Milad Ahmadian2, Sajad Ahmadian3

  • 1Institute for Intelligent Systems Research and Innovation, (IISRI), Deakin University, Geelong, Australia.

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Summary

This study introduces an enhanced deep learning method for detecting COVID-19 from chest X-rays. The approach combines a convolutional neural network with a k-nearest neighbors classifier and an optimized evolutionary algorithm for improved accuracy and efficiency.

Keywords:
COVID-19Convolutional neural networkCoronavirusDeep neuroevolution learningImage classificationK-nearest neighbor classifier

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Radiological imaging, including chest X-rays and CT scans, is crucial for diagnosing and monitoring COVID-19.
  • Manual analysis of these images for COVID-19 patterns is time-consuming and prone to errors.
  • Deep learning (DL) models offer automated analysis but require complex hyperparameter tuning.

Purpose of the Study:

  • To develop an effective and automated method for detecting COVID-19 using chest X-ray images.
  • To improve the accuracy and efficiency of DL models in COVID-19 detection.
  • To address the challenges of DL model fine-tuning through an optimized evolutionary algorithm.

Main Methods:

  • A convolutional neural network (CNN) was applied to chest X-ray images for COVID-19 detection.
  • The CNN's final Softmax layer was replaced with a k-nearest neighbors (KNN) classifier to leverage neighborhood labeling agreement.
  • A novel evolutionary algorithm, incorporating Cauchy Mutation, Evolutionary Boundary Constraint Handling, and a tent chaotic map, was developed to optimize CNN hyperparameters.

Main Results:

  • The proposed method demonstrated significant improvements in classification accuracy compared to existing models.
  • The integrated evolutionary algorithm effectively optimized CNN hyperparameters, enhancing performance.
  • The combination of CNN, KNN, and the optimized evolutionary algorithm led to a more efficient and accurate COVID-19 detection system.

Conclusions:

  • The developed method offers a promising automated solution for COVID-19 detection from chest X-rays.
  • Optimizing DL hyperparameters with advanced evolutionary algorithms can substantially boost diagnostic accuracy.
  • This approach represents a significant advancement in leveraging AI for infectious disease diagnostics.