• ISSN: 2349-6002
  • UGC Approved Journal No 47859
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Covid-19 Detection Using Chest X-Ray

  • Unique Paper ID: 155791
  • Volume: 9
  • Issue: 1
  • PageNo: 1788-1792
  • Abstract:
  • COVID-19 continues to have a devastating impact on the lives of people all around the world. It is vital to screen the affected patients in a timely and cost-effective manner in order to combat this disease. Radiological examination is one of the most plausible steps in achieving this goal, with chest X-Ray being the most readily available and least priced option. We present a Deep Convolutional Neural Network-based approach to detect COVID-19 +ve patients using chest X-Ray pictures in this research. The implementation of a semi-quantitative CXR assessment has resulted from the addition of useful assistance to clinicians and the stratification of disease risk. Both severity scores and CXR results diagnosed early stage COVID-19 disease in this study. CXRs abnormalities were detected in 278 of 350 patients (78%) at certain points of the disease course. The DarkNet model was used in our study as a classifier for the you only look once (YOLO) real time object detection system. We implemented 17 convolutional layers and introduced different filtering on each layer. We have created a graphical user interface (GUI) application for public use. This application can be used by any medical personnel on any computer to detect COVID +ve patients using Chest X-Ray images in a matter of seconds.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 9
  • Issue: 1
  • PageNo: 1788-1792

Covid-19 Detection Using Chest X-Ray

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8.01 (Year 2024)
UGC Approved
Journal no 47859

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