USE OF DEEP NEURAL NETWORKS WITH RADIOGRAPHIC IMAGES FOR AUTOMATED COVID-19 DETECTION

M JAVED; M ZAHRA; M ALI

doi:10.54112/bcsrj.v2023i1.249

Authors

M JAVED Department of Radiology, Children's Hospital & The Institute of Child Health Multan, Pakistan
M ZAHRA Department of Pediatric Radiology, Children's Hospital & The Institute of Child Health Multan, Pakistan
M ALI Department of Radiology, Children's Hospital & The Institute of Child Health Multan, Pakistan

DOI:

https://doi.org/10.54112/bcsrj.v2023i1.249

Keywords:

chest x-ray, automated detection, COVID-19, Deep neural network

Abstract

The prospective study was conducted in CH & ICH Multan from January 2021 to January 2022 to assess the significance of the proposed deep learning model that automatically uses X-rays to detect COVID-19. The development of the model proposed in the current study is based on the Darknet-19 model. When two classes are used, the proposed models detect COVID-19 infection. If three classes are used, the model classifies x-ray images as No finding, pneumonia, or COVID-19. First, the proposed model was used to classify X-ray images into Pneumonia, COVID-19, and No finding. Second, the model has been trained to detect two classes: No finding and COVID-19 categories. Our model achieved 87.06% and 97.88% accuracy for multiclass and binary tasks, respectively. Thus, it can be concluded that DarkCovidNet Deep Learning Model can be used for automated COVID-19 detection through X-ray images.

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