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Related Experiment Video

Updated: Nov 18, 2025

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Shallow Convolutional Neural Network for COVID-19 Outbreak Screening Using Chest X-rays.

Himadri Mukherjee1, Subhankar Ghosh2, Ankita Dhar1

  • 1Department of Computer Science, West Bengal State University, Kolkata, India.

Cognitive Computation
|February 10, 2021
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Summary
This summary is machine-generated.

A new lightweight Convolutional Neural Network (CNN) accurately detects COVID-19 from Chest X-rays (CXRs) with no false negatives. This AI tool shows promise for efficient mass screening of COVID-19 cases.

Keywords:
COVID-19Chest X-raysConvolutional neural networkDeep learningMass screening

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Chest X-rays (CXRs) are crucial for identifying COVID-19. Efficient AI tools are needed for mass screening.
  • Existing deep learning models can be computationally intensive for rapid COVID-19 detection.

Purpose of the Study:

  • To develop a computationally efficient, lightweight Convolutional Neural Network (CNN) for automated COVID-19 detection from CXRs.
  • To achieve high accuracy and zero false negatives in identifying COVID-19 positive cases.

Main Methods:

  • A shallow, lightweight CNN architecture was designed with fewer parameters.
  • The model was trained and validated on 321 COVID-19 positive CXRs and 5856 non-COVID-19 cases (normal, viral, bacterial pneumonia).
  • 5-fold cross-validation was employed on balanced and imbalanced datasets to ensure robust evaluation.

Main Results:

  • The proposed CNN achieved 99.69% accuracy and 1.0 sensitivity (AUC: 0.9995).
  • A very low false positive rate of 0.0015 was reported for COVID-19 negative cases.
  • The model demonstrated superior performance compared to other deep learning models and state-of-the-art methods.

Conclusions:

  • The developed lightweight CNN is highly effective for COVID-19 detection using CXRs.
  • The model's efficiency and accuracy make it suitable for large-scale COVID-19 screening.
  • This AI-driven approach offers a promising solution for rapid and reliable diagnosis.