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Detection of COVID-19 Based on Chest X-rays Using Deep Learning
| Content Provider | MDPI |
|---|---|
| Author | Gouda, Walaa Almurafeh, Maram Humayun, Mamoona Jhanjhi, Noor Zaman |
| Copyright Year | 2022 |
| Description | 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. |
| Starting Page | 343 |
| e-ISSN | 22279032 |
| DOI | 10.3390/healthcare10020343 |
| Journal | Healthcare |
| Issue Number | 2 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-02-10 |
| Access Restriction | Open |
| Subject Keyword | Healthcare Industrial Engineering Covid-19 Chest X-ray Pneumonia Deep Transfer Learning Neural Network (nn) |
| Content Type | Text |
| Resource Type | Article |
