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Abstract

There has been a COVID-19 pandemic since December 2019, and successful medical treatment for COVID-19 patients requires rapid and accurate diagnosis. Fighting the COVID-19 pandemic requires an automated system that uses deep transfer learning to diagnose the virus on chest X-ray (CXR). CXR are frequently utilized in healthcare because they offer the potential for rapid and accurate disease diagnosis. Automated computer-aided diagnosis (CAD) systems incorporate ML or deep learning to enhance efficiency and accuracy, hence reducing future problems. Numerous AI systems based on deep learning can be employed for diagnosis; among the most widely used is the CNN, which was first developed and has demonstrated encouraging accuracy in identifying COVID-19 confirmed patients using CXR pictures. Through using X-ray images, this work will design ML and deep learning to provide faster diagnostics for COVID-19 infection. As a result, the deep transfer learning technique uses an existing model first, then applies the needed data to it again. Where a Densenet201transfer learning model was utilized, which is one of a DL techniques, as feature extraction and its combination with multilayer perceptron algorithm; these technique were applied to a data set of a National Institute Health (NIH), where several performance measures were utilized, such as precision, precision, specificity and sensitivity, as an experiment proved the efficiency of the algorithm used in terms of accuracy by 98.82%. These outcomes are encouraging when compared to other DL models that were trained on the identical dataset.

Keywords

Chest X-ray, Deep learning, Machine learning, Densenet201, Multilayer perceptron algorithm

Subject Area

Computer Science

Article Type

Article

First Page

1034

Last Page

1048

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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