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CSGBBNet: An Explainable Deep Learning Framework for COVID-19 Detection.

Xu-Jing Yao1, Zi-Quan Zhu2, Shui-Hua Wang1

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

Diagnostics (Basel, Switzerland)
|September 28, 2021
PubMed
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A new deep learning framework, CSGBBNet, efficiently analyzes lung CT scans for COVID-19 detection. This AI tool improves diagnostic accuracy, complementing PCR tests and aiding in early disease identification.

Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis
  • Infectious Disease Diagnostics

Background:

  • The COVID-19 pandemic necessitates improved diagnostic tools beyond nucleic acid polymerase chain reaction (PCR) testing.
  • Lung CT examinations offer a valuable supplementary method for detecting COVID-19, but traditional analysis is time-consuming.
  • Accurate and rapid diagnosis is crucial for managing the ongoing global health crisis.

Purpose of the Study:

  • To develop an efficient deep learning framework for the binary classification of COVID-19 from lung CT images.
  • To address the limitations of traditional CT analysis by creating an automated and rapid diagnostic aid.
  • To integrate artificial intelligence with biomedical research for enhanced COVID-19 detection.

Main Methods:

Keywords:
Bayesian OptimizationCOVID-19chest CTconvolutional neural networkdeep learningmachine learning

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  • Developed CSGBBNet, a novel deep learning framework combining COVID-Seg for preprocessing and GBBNet for classification.
  • Utilized lung CT scan images for the binary classification task of COVID-19 detection.
  • Evaluated the framework's performance through five runs with random seeds on a test set.

Main Results:

  • The CSGBBNet framework achieved high performance metrics: mean accuracy of 98.49%, sensitivity of 99.00%, specificity of 97.95%, precision of 98.10%, and F1 score of 98.51%.
  • The model demonstrated rapid analysis capabilities for CT scan images, providing effective results for COVID-19 detection.
  • CSGBBNet outperformed seven previously published state-of-the-art methods in COVID-19 image classification.

Conclusions:

  • The developed CSGBBNet framework offers an efficient and accurate AI-driven solution for detecting COVID-19 using lung CT images.
  • This approach effectively assists in diagnosing COVID-19, potentially overcoming limitations associated with PCR testing.
  • The study highlights the potential of integrating AI and biomedical research for advancing infectious disease diagnostics.