Volume 10, Issue 2 (9-2023)
jhbmi 2023, 10(2): 111-124 |
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Roustaei M, Giveki D. Diagnosis of COVID-19 Disease in X-ray Images Based on Deep Learning Methods and Combining Classifiers. jhbmi 2023; 10 (2) :111-124
URL: http://jhbmi.ir/article-1-733-en.html
URL: http://jhbmi.ir/article-1-733-en.html
PhD in Computer Engineering, Assistant Professor, Department of Computer Engineering, Faculty of Engineering, Malayer University, Malayer, Iran
Abstract: (1071 Views)
Introduction: COVID-19 is a new virus that causes infection in the upper respiratory tract and lungs, and the number of deaths due to the disease has increased daily on the scale of a global epidemic. Chest X-ray images have been useful for monitoring various lung diseases and have recently been used to monitor COVID-19 disease.
Method: In this research, a multi-stage process was used to recognize COVID-19 from X-ray images. In the first stage, pre-processing was done to normalize the data. In the second step, which is the most important step of the proposed method, feature extraction was done. The feature extraction operation was based on deep learning networks. After feature extraction, machine learning algorithms were used to classify images. The algorithms used in this section are support vector machine, nearest neighbor, and decision tree algorithms. The results of these categories are combined in the fourth step based on the majority vote.
Results: The parameters used in this research are among the classification parameters, including precision, accuracy, recall, and F-criterion, which were obtained as 96.5, 92.25, 94, and 93, respectively.
Conclusion: The results of the experiments show the acceptable efficiency of the proposed method because, in addition to reducing the calculations by the separable layer, the combination of categories and their weighting has been used to obtain the final result.
Method: In this research, a multi-stage process was used to recognize COVID-19 from X-ray images. In the first stage, pre-processing was done to normalize the data. In the second step, which is the most important step of the proposed method, feature extraction was done. The feature extraction operation was based on deep learning networks. After feature extraction, machine learning algorithms were used to classify images. The algorithms used in this section are support vector machine, nearest neighbor, and decision tree algorithms. The results of these categories are combined in the fourth step based on the majority vote.
Results: The parameters used in this research are among the classification parameters, including precision, accuracy, recall, and F-criterion, which were obtained as 96.5, 92.25, 94, and 93, respectively.
Conclusion: The results of the experiments show the acceptable efficiency of the proposed method because, in addition to reducing the calculations by the separable layer, the combination of categories and their weighting has been used to obtain the final result.
Keywords: COVID-19, X-ray Images, Feature Extraction, Deep Learning Networks, Machine Learning, Classification
Type of Study: Original Article |
Subject:
Artificial Intelligence in Healthcare
Received: 2022/11/21 | Accepted: 2023/09/10
Received: 2022/11/21 | Accepted: 2023/09/10
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