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. 2022 Jan 25:2022:4130674.
doi: 10.1155/2022/4130674. eCollection 2022.

Optimal Deep-Learning-Enabled Intelligent Decision Support System for SARS-CoV-2 Classification

Ashit Kumar Dutta  1 , Nasser Ali Aljarallah  2   3 , T Abirami  4 , M Sundarrajan  5 , Seifedine Kadry  6 , Yunyoung Nam  7 , Chang-Won Jeong  8
Affiliations

Optimal Deep-Learning-Enabled Intelligent Decision Support System for SARS-CoV-2 Classification

Ashit Kumar Dutta et al. J Healthc Eng. .

Abstract

Intelligent decision support systems (IDSS) for complex healthcare applications aim to examine a large quantity of complex healthcare data to assist doctors, researchers, pathologists, and other healthcare professionals. A decision support system (DSS) is an intelligent system that provides improved assistance in various stages of health-related disease diagnosis. At the same time, the SARS-CoV-2 infection that causes COVID-19 disease has spread globally from the beginning of 2020. Several research works reported that the imaging pattern based on computed tomography (CT) can be utilized to detect SARS-CoV-2. Earlier identification and detection of the diseases is essential to offer adequate treatment and avoid the severity of the disease. With this motivation, this study develops an efficient deep-learning-based fusion model with swarm intelligence (EDLFM-SI) for SARS-CoV-2 identification. The proposed EDLFM-SI technique aims to detect and classify the SARS-CoV-2 infection or not. Also, the EDLFM-SI technique comprises various processes, namely, data augmentation, preprocessing, feature extraction, and classification. Moreover, a fusion of capsule network (CapsNet) and MobileNet based feature extractors are employed. Besides, a water strider algorithm (WSA) is applied to fine-tune the hyperparameters involved in the DL models. Finally, a cascaded neural network (CNN) classifier is applied for detecting the existence of SARS-CoV-2. In order to showcase the improved performance of the EDLFM-SI technique, a wide range of simulations take place on the COVID-19 CT data set and the SARS-CoV-2 CT scan data set. The simulation outcomes highlighted the supremacy of the EDLFM-SI technique over the recent approaches.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Overall block diagram of EDLFM-SI model.
Figure 2
Figure 2
Structure of the CapsNet model.
Figure 3
Figure 3
Sample images.
Figure 4
Figure 4
Confusion matrix of EDLFM-SI model under data set-1: (a) run-1, (b) run-2, (c) run-3, (d) run-4, (e) run-5, (f) run-6, (g) run-7, (h) run-8, (i) run-9, and (j) run-10.
Figure 5
Figure 5
Result analysis of EDLFM-SI model under data set-1.
Figure 6
Figure 6
Accuracy analysis of EDLFM-SI model under data set-1.
Figure 7
Figure 7
Loss analysis of EDLFM-SI model under data set-1.
Figure 8
Figure 8
Comparative analysis of EDLFM-SI model under data set-1.
Figure 9
Figure 9
Confusion matrix analysis of EDLFM-SI model under data set-2: (a) run-1, (b) run-2, (c) run-3, (d) run-4, (e) run-5, (f) run-6, (g) run-7, (h) run-8, (i) run-9, and (j) run-10.
Figure 10
Figure 10
Result analysis of EDLFM-SI model under data set-2.
Figure 11
Figure 11
Accuracy analysis of EDLFM-SI model under data set-2.
Figure 12
Figure 12
Loss analysis of EDLFM-SI model under data set-2.
Figure 13
Figure 13
Comparative analysis of EDLFM-SI model under data set-2.
See this image and copyright information in PMC

References

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