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Detection and classification of COVID-19 by using faster R-CNN and mask R-CNN on CT images.

Neural computing & applications·2023
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Deep learning-based approach for detecting COVID-19 in chest X-rays.

M Emin Sahin1

  • 1Department of Computer Engineering, Yozgat Bozok University, Turkey.

Biomedical Signal Processing and Control
|July 20, 2022
PubMed
Summary
This summary is machine-generated.

A novel Convolutional Neural Network (CNN) model accurately detects COVID-19 from X-ray images with 96.71% accuracy. This AI tool aids clinicians in diagnosing COVID-19, improving accessibility to testing.

Keywords:
CNNCOVID-19Deep LearningMobileNetv2ResNet50X-ray images

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • 2019 Coronavirus (COVID-19) poses a significant global health challenge.
  • Accurate detection of COVID-19 in chest X-ray images is vital for patient diagnosis, evaluation, and treatment.
  • Radiologists face challenges in reporting diagnostic uncertainty.

Purpose of the Study:

  • To propose a novel Convolutional Neural Network (CNN) model for automated COVID-19 detection using chest X-ray images.
  • To evaluate the CNN model's performance for binary classification (COVID-19 vs. Normal).
  • To compare the proposed model against established architectures like MobileNetv2 and ResNet50.

Main Methods:

  • Development of a novel CNN model for COVID-19 identification.
  • Utilized a dataset comprising 13,824 chest X-ray images.
  • Comparative analysis with pre-trained MobileNetv2 and ResNet50 models.

Main Results:

  • The proposed CNN model achieved 96.71% accuracy in identifying COVID-19.
  • An F1-score of 91.89% was obtained for the proposed model.
  • The novel CNN model demonstrated superior performance compared to existing COVID-19 detection algorithms.

Conclusions:

  • The developed CNN model serves as a reliable diagnostic tool for COVID-19 detection from X-ray images.
  • This AI-driven approach can assist clinicians in making informed diagnostic decisions.
  • The model has the potential to enhance the accessibility of diagnostic testing for COVID-19.