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Related Experiment Video

Updated: Oct 11, 2025

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Uncertainty-aware convolutional neural network for COVID-19 X-ray images classification.

Mahesh Gour1, Sweta Jain1

  • 1Maulana Azad National Institute of Technology, Bhopal, MP, 462003, India.

Computers in Biology and Medicine
|November 30, 2021
PubMed
Summary

This study introduces UA-ConvNet, an uncertainty-aware deep learning model for COVID-19 detection from chest X-rays. The model provides reliable predictions with uncertainty estimation, outperforming existing methods.

Keywords:
COVID-19 automatic screening uncertainty estimation Monte Carlo dropout pre-trained EfficientNet CNN Chest X-ray images

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Area of Science:

  • Medical Image Analysis
  • Artificial Intelligence
  • Radiology

Background:

  • Deep learning (DL) models have advanced medical image analysis, including COVID-19 detection from chest X-rays (CXRs).
  • Existing DL models lack inherent uncertainty estimation, a critical feature for medical diagnostics.

Purpose of the Study:

  • To develop an uncertainty-aware convolutional neural network (UA-ConvNet) for automated COVID-19 detection from CXRs.
  • To estimate the uncertainty associated with DL model predictions in medical imaging.

Main Methods:

  • Fine-tuning the EfficientNet-B3 model on CXR datasets.
  • Employing Monte Carlo (MC) dropout during inference for multiple forward passes.
  • Calculating mean and entropy from the predictive distribution to quantify uncertainty.

Main Results:

  • The UA-ConvNet achieved a G-mean of 98.02% and 99.16% for multi-class and binary classification, respectively, on different datasets.
  • Sensitivity reached 98.15% (multi-class) and 99.30% (binary).
  • The model demonstrated superior performance compared to existing methods in diagnosing COVID-19 from CXRs.

Conclusions:

  • The proposed UA-ConvNet effectively detects COVID-19 from CXRs while providing crucial uncertainty estimates.
  • This approach enhances the reliability of AI in medical diagnostics.
  • The method shows significant potential for improving automated screening of infectious diseases.