期刊
HEALTHCARE
卷 10, 期 2, 页码 -出版社
MDPI
DOI: 10.3390/healthcare10020343
关键词
COVID-19; chest X-ray; pneumonia; deep transfer learning; neural network (NN)
资金
- Deanship of Scientific Research at Jouf University [DSR-2021-02-0370]
This study proposes two novel deep learning methods for detecting COVID-19 using chest X-ray images, which achieve reliable diagnosis through preprocessing and utilizing a pre-trained model. The proposed system outperforms existing methods in various metrics, as demonstrated on public benchmark datasets.
The coronavirus disease (COVID-19) is rapidly spreading around the world. Early diagnosis and isolation of COVID-19 patients has proven crucial in slowing the disease's spread. One of the best options for detecting COVID-19 reliably and easily is to use deep learning (DL) strategies. Two different DL approaches based on a pertained neural network model (ResNet-50) for COVID-19 detection using chest X-ray (CXR) images are proposed in this study. Augmenting, enhancing, normalizing, and resizing CXR images to a fixed size are all part of the preprocessing stage. This research proposes a DL method for classifying CXR images based on an ensemble employing multiple runs of a modified version of the Resnet-50. The proposed system is evaluated against two publicly available benchmark datasets that are frequently used by several researchers: COVID-19 Image Data Collection (IDC) and CXR Images (Pneumonia). The proposed system validates its dominance over existing methods such as VGG or Densnet, with values exceeding 99.63% in many metrics, such as accuracy, precision, recall, F1-score, and Area under the curve (AUC), based on the performance results obtained.
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