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. 2022 Jul 14:2022:4254631.
doi: 10.1155/2022/4254631. eCollection 2022.

COVID-19 Classification from Chest X-Ray Images: A Framework of Deep Explainable Artificial Intelligence

Muhammad Attique Khan  1 , Marium Azhar  2 , Kainat Ibrar  2 , Abdullah Alqahtani  3 , Shtwai Alsubai  3 , Adel Binbusayyis  3 , Ye Jin Kim  4 , Byoungchol Chang  5
Affiliations

COVID-19 Classification from Chest X-Ray Images: A Framework of Deep Explainable Artificial Intelligence

Muhammad Attique Khan et al. Comput Intell Neurosci. .

Abstract

COVID-19 detection and classification using chest X-ray images is a current hot research topic based on the important application known as medical image analysis. To halt the spread of COVID-19, it is critical to identify the infection as soon as possible. Due to time constraints and the expertise of radiologists, manually diagnosing this infection from chest X-ray images is a difficult and time-consuming process. Artificial intelligence techniques have had a significant impact on medical image analysis and have also introduced several techniques for COVID-19 diagnosis. Deep learning and explainable AI have shown significant popularity among AL techniques for COVID-19 detection and classification. In this work, we propose a deep learning and explainable AI technique for the diagnosis and classification of COVID-19 using chest X-ray images. Initially, a hybrid contrast enhancement technique is proposed and applied to the original images that are later utilized for the training of two modified deep learning models. The deep transfer learning concept is selected for the training of pretrained modified models that are later employed for feature extraction. Features of both deep models are fused using improved canonical correlation analysis that is further optimized using a hybrid algorithm named Whale-Elephant Herding. Through this algorithm, the best features are selected and classified using an extreme learning machine (ELM). Moreover, the modified deep models are utilized for Grad-CAM visualization. The experimental process was conducted on three publicly available datasets and achieved accuracies of 99.1, 98.2, and 96.7%, respectively. Moreover, the ablation study was performed and showed that the proposed accuracy is better than the other methods.

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Conflict of interest statement

The authors declare that there are no conflicts of interest.

Figures

Figure 1
Figure 1
Sample image of COVID-19 Radiography dataset.
Figure 2
Figure 2
Main architecture of proposed framework for classification of COVID-19 infected Chest X-ray images.
Figure 3
Figure 3
Sample images of the proposed contrast enhancement technique.
Figure 4
Figure 4
Deep transfer learning-based model training.
Figure 5
Figure 5
Labeled results of the proposed method.
Figure 6
Figure 6
Grad-CAM-based visualization.
Figure 7
Figure 7
Confusion matrix of ELM classifier on COVID-19 Radiography database using the proposed framework.
Figure 8
Figure 8
Confusion matrix of ELM classifier on Covid-GAN and Covid-Net mini Chest X-ray dataset using the proposed framework.
Figure 9
Figure 9
Confusion matrix of ELM classifier on Chest X-ray dataset using the proposed framework.
Figure 10
Figure 10
Comparison of different neural nets in terms of accuracy on COVID-19 Radiography database.
Figure 11
Figure 11
Comparison of different neural nets in terms of accuracy on Covid-GAN and Covid-Net mini Chest X-ray dataset.
Figure 12
Figure 12
Comparison of different neural nets in terms of accuracy on Chest X-ray dataset.
See this image and copyright information in PMC

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