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Related Experiment Video

Updated: Sep 6, 2025

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CVD-HNet: Classifying Pneumonia and COVID-19 in Chest X-ray Images Using Deep Network.

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  • 1Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamilnadu 641 407 India.

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Summary
This summary is machine-generated.

This study introduces novel deep Convolutional Neural Network (CNN) models, CVD-HNet1 and CVD-HNet2, for classifying COVID-19 pneumonia from X-ray images. The AI-driven approach achieves high accuracy, aiding early detection of the disease.

Keywords:
AccuracyCNNCOVID 19Deep learningMatthews correlation coefficientX-ray

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Deep Learning

Background:

  • Computer-assisted analysis in medical imaging faces challenges in image interpretation.
  • Advances in Artificial Intelligence (AI), particularly Deep Learning (DL), are crucial for classifying, detecting, and quantifying medical image anomalies.

Purpose of the Study:

  • To develop and evaluate novel deep Convolutional Neural Network (CNN) architectures for classifying COVID-19 infected X-ray images.
  • To provide an AI-based tool for early diagnosis and detection of COVID-19 pneumonia.

Main Methods:

  • Design of two customized CNN architectures: CVD-HNet1 and CVD-HNet2.
  • Utilizing boundary- and region-based operations combined with systematic convolution processes for image analysis.
  • Classification of COVID-19 positive and negative X-ray images.

Main Results:

  • The proposed CVD-HNet model achieved excellent classification performance.
  • Achieved 98% accuracy, 0.99 F1 Score, and 0.97 Matthews Correlation Coefficient (MCC).
  • Demonstrated impressive classification accuracy on a limited dataset, with potential for improvement with more data.

Conclusions:

  • The novel CVD-HNet model shows significant potential for accurate COVID-19 detection from X-ray images.
  • The developed AI tool can assist radiologists in early diagnosis and management of COVID-19.
  • Further training with larger datasets may enhance the model's performance.