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CvDeep-COVID-19 Detection Model.

Vaishali Arjun Ingle1, Prashant Mahadev Ambad2

  • 1Department of Computer Science and IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra India.

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|March 23, 2021
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Summary
This summary is machine-generated.

This study introduces CvDeep, a machine learning model for rapid COVID-19 detection using X-ray images. The model achieves 95% accuracy, aiding early diagnosis and improving patient survival rates.

Keywords:
AlexNetCOVID-19CoronaDeep learningDenseNetResNetSquzeeNetX-ray images

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • The global COVID-19 pandemic necessitates rapid diagnostic tools.
  • X-ray imaging offers valuable data for assessing lung infections.
  • Machine learning can address healthcare access limitations in remote areas.

Purpose of the Study:

  • To design and evaluate CvDeep, a novel model for COVID-19 detection from X-ray images.
  • To enhance the accuracy of early COVID-19 detection using deep learning.
  • To provide an assistive tool for radiologists and improve patient outcomes.

Main Methods:

  • Development of the CvDeep model utilizing pre-trained deep learning architectures (AlexNet, SqueezeNet, ResNet, DenseNet).
  • Preprocessing of X-ray images for enhanced diagnostic accuracy.
  • Evaluation of the model's performance by expert radiologists.

Main Results:

  • The CvDeep model achieved a 95% accuracy rate for COVID-19 detection.
  • The study demonstrated the effectiveness of pre-trained deep learning models in improving early detection accuracy.
  • The model shows potential for immediate patient hospitalization decisions.

Conclusions:

  • CvDeep offers a highly accurate and efficient method for COVID-19 detection via X-ray analysis.
  • The model can significantly aid in the early diagnosis and management of COVID-19 patients.
  • Sharing the dataset can further assist radiologists and advance research in AI-driven diagnostics.