Oct 22, 2021
Multi-layer Hybrid Classification Model of COVID-19 Chest X-ray Images
- Thanakorn Poomkur1
- 1Data Science, College of Digital Science, Prince of Songkla University, Thailand
- ChengZhu Science
Protocol Citation: Thanakorn Poomkur 2021. Multi-layer Hybrid Classification Model of COVID-19 Chest X-ray Images. protocols.io https://dx.doi.org/10.17504/protocols.io.by9kpz4w
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it’s working
Created: October 20, 2021
Last Modified: October 22, 2021
Protocol Integer ID: 54284
Keywords: Artificial Intelligence, computer-aided diagnosis, Machine Learning, COVID-19, coronavirus disease, lung inflammation, aided diagnosis, radiography, detailed comparison of the mlhc, diagnosis, infection, classification result
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Abstract
The coronavirus disease of 2019(COVID-19) has been declared a pandemic and has raised worldwide concern. Lung inflammation and respiratory failure are commonly observed in moderate-to-severe cases. Radiography or chest X-ray imaging is compulsory for diagnosis, and interpretation is commonly performed by skilled medical specialists. In this study, we propose anew computer-aided diagnosis (CADx) tool for identifying chest X-ray images of COVID-19 infection using a multi-layer hybrid classification model (MLHC). The MLHC-COVID-19 consists of two layers, Layer I: Healthy and non-Healthy; Layer II: COVID-19 and non-COVID-19. The MLHC-COVID-19 was evaluated in real COVID-19 cases. The classification results showed promising performance comparable with other existing techniques considering the accuracy, sensitivity, and specificity of 96.20%, 96.20%, and 0.971%, respectively. This demonstrates the effectiveness of the MLHC-COVID-19 in classifying chest X-ray images, enhancing the accuracy of chest X-ray image interpretation with a reduction in the interpretation time. Furthermore, a detailed comparison of the MLHC-COVID-19 with other techniques has been presented.
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