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Related Experiment Video

Updated: Nov 15, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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COVID-19 Detection from Chest X-ray Images Using Feature Fusion and Deep Learning.

Nur-A- Alam1, Mominul Ahsan2, Md Abdul Based3

  • 1Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail 1902, Bangladesh.

Sensors (Basel, Switzerland)
|March 6, 2021
PubMed
Summary

This study introduces a machine vision approach using fused features from chest X-rays for accurate COVID-19 detection. The method achieved high accuracy, outperforming individual techniques and aiding early diagnosis.

Keywords:
COVID-19X-ray imageconvolutional neural network (CNN)deep learninghistogram-oriented gradient (HOG)watershed segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • COVID-19 poses a significant global health threat, necessitating accurate and early detection methods.
  • Diagnosis often relies on chest X-rays, but identifying asymptomatic cases remains challenging.
  • Timely diagnosis is crucial for reducing mortality rates associated with COVID-19.

Purpose of the Study:

  • To develop and evaluate a machine vision approach for detecting COVID-19 from chest X-ray images.
  • To enhance diagnostic accuracy, particularly for subtle or asymptomatic cases.
  • To improve upon existing COVID-19 detection methods through feature fusion and deep learning.

Main Methods:

  • Utilized a machine vision approach combining Histogram-Oriented Gradient (HOG) and Convolutional Neural Network (CNN) features.
  • Employed CNN (VGGNet) for model training and classification.
  • Applied Modified Anisotropic Diffusion Filtering (MADF) for noise reduction and edge preservation.
  • Implemented a watershed segmentation algorithm to identify significant regions in X-ray images.
  • Used 5-fold cross-validation to mitigate overfitting and validate model performance.

Main Results:

  • The proposed feature fusion method achieved a testing accuracy of 99.49%, specificity of 95.7%, and sensitivity of 93.65%.
  • The CNN-based classification outperformed traditional methods like Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM).
  • K-fold cross-validation confirmed the superiority of the feature fusion technique (98.36% accuracy) over individual HOG (87.34%) and CNN (93.64%) methods.

Conclusions:

  • The developed deep learning-based feature fusion technique demonstrates high efficacy for COVID-19 detection from chest X-rays.
  • This approach offers a promising tool for early and accurate diagnosis, potentially improving patient outcomes.
  • The study highlights the advantages of combining complementary feature extraction methods for robust medical image analysis.