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Related Experiment Videos

Covid-19 Diagnosis by WE-SAJ.

Wei Wang1, Xin Zhang2, Shui-Hua Wang1

  • 1School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK.

Systems Science & Control Engineering
|December 26, 2022
PubMed
Summary
This summary is machine-generated.

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This study introduces WE-SAJ, a deep learning model for COVID-19 diagnosis using CT scans. The AI model shows high accuracy in distinguishing infected patients, aiding rapid medical resource allocation.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • The COVID-19 pandemic presents a critical challenge due to rapidly increasing patient numbers and strained medical resources.
  • Fast and accurate diagnosis of COVID-19 is essential for effective patient management and resource allocation.
  • Artificial intelligence (AI) offers potential for rapid and accurate classification of medical images, including CT scans for COVID-19 detection.

Purpose of the Study:

  • To propose a novel deep learning model, WE-SAJ, for the accurate classification of COVID-19 from CT images.
  • To evaluate the performance of the proposed WE-SAJ model against a Jaya-based model for medical image classification.
  • To demonstrate the effectiveness of the adaptive Jaya algorithm in training AI models for COVID-19 diagnosis.

Main Methods:

Keywords:
COVID-19Deep LearningDiagnosisJayaSelf-adaptive JayaWavelet Entropy

Related Experiment Videos

  • Development of a deep learning model (WE-SAJ) incorporating wavelet entropy for feature extraction.
  • Utilization of two-layer Feedforward Neural Networks (FNNs) for image classification.
  • Application of the adaptive Jaya algorithm for training the deep learning model.

Main Results:

  • The WE-SAJ model achieved a sensitivity of 85.47±1.84, specificity of 87.23±1.67, and accuracy of 86.35±0.70.
  • The model demonstrated superior performance compared to the Jaya-based model in COVID-19 classification.
  • Key performance metrics include precision (87.03±1.34), F1 score (86.23±0.77), and Matthews correlation coefficient (72.75±1.38).

Conclusions:

  • The proposed WE-SAJ deep learning model shows significant potential for AI-driven COVID-19 diagnosis using CT images.
  • The adaptive Jaya algorithm proves effective for training AI models in medical image classification tasks, outperforming the standard Jaya algorithm.
  • AI techniques, particularly the developed WE-SAJ model, can aid in the rapid and accurate diagnosis of COVID-19, supporting healthcare systems.