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Related Experiment Video

Updated: Oct 13, 2025

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Deep learning-based improved snapshot ensemble technique for COVID-19 chest X-ray classification.

Samson Anosh Babu P1, Chandra Sekhara Rao Annavarapu1

  • 1Department of Computer Science and Engineering, Indian Institute of Technology (ISM), Dhanbad, 826004 India.

Applied Intelligence (Dordrecht, Netherlands)
|November 12, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an improved deep learning model for classifying COVID-19 from chest X-rays. The efficient Snapshot Ensemble technique with ResNet-50 transfer learning achieved high accuracy, aiding rapid diagnosis.

Keywords:
COVID-19Chest X-rayClassificationDeep learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Disease Diagnostics

Background:

  • The COVID-19 pandemic highlighted the need for rapid and accurate diagnostic tools.
  • Manual interpretation of chest X-rays for COVID-19 diagnosis is time-consuming and resource-intensive.
  • While CT scans are preferred, chest X-rays offer a faster, cheaper, and more accessible alternative.

Purpose of the Study:

  • To develop an efficient deep learning model for automated COVID-19 classification using chest X-ray images.
  • To leverage transfer learning with a pre-trained ResNet-50 model for improved diagnostic performance.
  • To evaluate the proposed model's efficacy against existing methods for COVID-19 detection.

Main Methods:

  • Implementation of an improved Snapshot Ensemble deep learning technique.
  • Utilization of the ResNet-50 model pre-trained on a large dataset for transfer learning.
  • Training and validation on a publicly available dataset of 2905 chest X-ray images (COVID-19, viral pneumonia, normal).

Main Results:

  • The model achieved high performance metrics, including 97% specificity, 95% F1-score, and 95% classification accuracy.
  • Performance was evaluated using Area Under the Receiver Operating Characteristic curve (AU-ROC), Area Under the Precision-Recall curve (AU-PR), and Jaccard Index.
  • The proposed deep learning approach demonstrated superior performance compared to several existing methods.

Conclusions:

  • The developed deep learning model offers a suitable and efficient approach for COVID-19 classification from chest X-rays.
  • This automated method can significantly aid radiologists and healthcare professionals in faster and more accurate diagnosis.
  • The integration of Snapshot Ensemble and transfer learning shows promise for improving diagnostic capabilities in public health crises.