Elsevier

Applied Soft Computing

Volume 132, January 2023, 109851
Applied Soft Computing

Automatic detection of Covid-19 from chest X-ray and lung computed tomography images using deep neural networks and transfer learning

https://doi.org/10.1016/j.asoc.2022.109851Get rights and content

Highlights

  • EfficientNet and MixNet are suitable for detection of Covid-19 from chest X-ray and lung CT images.

  • Transfer learning is beneficial to the detection of Covid-19.

  • The proposed system significantly outperforms various state-of-the-art approaches.

  • It has been evaluated using different quality metrics.

Abstract

The world has been undergoing the most ever unprecedented circumstances caused by the coronavirus pandemic, which is having a devastating global effect in different aspects of life. Since there are not effective antiviral treatments for Covid-19 yet, it is crucial to early detect and monitor the progression of the disease, thereby helping to reduce mortality. While different measures are being used to combat the virus, medical imaging techniques have been examined to support doctors in diagnosing the disease. In this paper, we present a practical solution for the detection of Covid-19 from chest X-ray (CXR) and lung computed tomography (LCT) images, exploiting cutting-edge Machine Learning techniques. As the main classification engine, we make use of EfficientNet and MixNet, two recently developed families of deep neural networks. Furthermore, to make the training more effective and efficient, we apply three transfer learning algorithms. The ultimate aim is to build a reliable expert system to detect Covid-19 from different sources of images, making it be a multi-purpose AI diagnosing system. We validated our proposed approach using four real-world datasets. The first two are CXR datasets consist of 15,000 and 17,905 images, respectively. The other two are LCT datasets with 2,482 and 411,528 images, respectively. The five-fold cross-validation methodology was used to evaluate the approach, where the dataset is split into five parts, and accordingly the evaluation is conducted in five rounds. By each evaluation, four parts are combined to form the training data, and the remaining one is used for testing. We obtained an encouraging prediction performance for all the considered datasets. In all the configurations, the obtained accuracy is always larger than 95.0%. Compared to various existing studies, our approach yields a substantial performance gain. Moreover, such an improvement is statistically significant.

Keywords

COVID-19
AI Diagnosis systems
Expert systems
Chest X-ray image
Lung CT images

Data availability

The authors do not have permission to share data.

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