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. 2022 Sep 6:2022:7451551.
doi: 10.1155/2022/7451551. eCollection 2022.

Automatic Detection of Cases of COVID-19 Pneumonia from Chest X-ray Images and Deep Learning Approaches

Fahima Hajjej  1 , Sarra Ayouni  1 , Malek Hasan  2   3 , Tanvir Abir  4
Affiliations

Automatic Detection of Cases of COVID-19 Pneumonia from Chest X-ray Images and Deep Learning Approaches

Fahima Hajjej et al. Comput Intell Neurosci. .

Abstract

Machine learning has already been used as a resource for disease detection and health care as a complementary tool to help with various daily health challenges. The advancement of deep learning techniques and a large amount of data-enabled algorithms to outperform medical teams in certain imaging tasks, such as pneumonia detection, skin cancer classification, hemorrhage detection, and arrhythmia detection. Automated diagnostics, which are enabled by images extracted from patient examinations, allow for interesting experiments to be conducted. This research differs from the related studies that were investigated in the experiment. These works are capable of binary categorization into two categories. COVID-Net, for example, was able to identify a positive case of COVID-19 or a healthy person with 93.3% accuracy. Another example is CHeXNet, which has a 95% accuracy rate in detecting cases of pneumonia or a healthy state in a patient. Experiments revealed that the current study was more effective than the previous studies in detecting a greater number of categories and with a higher percentage of accuracy. The results obtained during the model's development were not only viable but also excellent, with an accuracy of nearly 96% when analyzing a chest X-ray with three possible diagnoses in the two experiments conducted.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Example chest X-ray image of: (a) Healthy. (b) Bacteremia. (c) Viral pneumonia. (d) COVID-19 viral infection.
Figure 2
Figure 2
Building blocks of a traditional CNN.
Figure 3
Figure 3
Example of the VGG-19 model network architecture.
Figure 4
Figure 4
Example of images discarded in the selection process because they contain noise.
Figure 5
Figure 5
Model Accuracy. (a) First experiment. (b) Second experiment.
Figure 6
Figure 6
Confusion matrix. (a) First experiment. (b) Second experiment.
Figure 7
Figure 7
Receiver Operation Characteristic Curve (ROC). (a) First experiment. (b) Second experiment. (Class 0 = Bacteremia group; Class 1 = COVID-19 group and Class 2 = Healthy group).
See this image and copyright information in PMC

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