|تعداد مشاهده مقاله||2,477,359|
|تعداد دریافت فایل اصل مقاله||1,746,054|
|Journal of Electrical and Computer Engineering Innovations (JECEI)|
|مقاله 14، دوره 10، شماره 2، مهر 2022، صفحه 411-424 اصل مقاله (1.24 M)|
|نوع مقاله: Original Research Paper|
|شناسه دیجیتال (DOI): 10.22061/jecei.2022.8200.491|
|I. Zabbah* 1؛ K. Layeghi2؛ Reza Ebrahimpour3|
|1Department of Computer Engineering, Islamic Azad University of Tehran North Branch, Tehran, Iran.|
|2Department of Computer Engineering, Islamic Azad University of North Tehran Branch, Tehran, Iran.|
|3Artificial Intelligence Department, Faculty of Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.|
|تاریخ دریافت: 12 آبان 1400، تاریخ بازنگری: 24 بهمن 1400، تاریخ پذیرش: 25 بهمن 1400|
|Background and Objectives: COVID-19 disease still has a devastating effect on society health. The use of X-ray images is one of the most important methods of diagnosing the disease. One of the challenges specialists are faced is no diagnosing in time. Using Deep learning can reduce the diagnostic error of COVID-19 and help specialists in this field. |
Methods: The aim of this study is to provide a method based on a combination of deep learning(s) in parallel so that it can lead to more accurate results in COVID-19 disease by gathering opinions. In this research, 4 pre-trained (fine-tuned) deep model have been used. The dataset of this study is X-ray images from Github containing 1125 samples in 3 classes include normal, COVID-19 and pneumonia contaminated.
Results: In all networks, 70% of the samples were used for training and 30% for testing. To ensure accuracy, the K-fold method was used in the training process. After modeling and comparing the generated models and recording the results, the accuracy of diagnosis of COVID-19 disease showed 84.3% and 87.2% when learners were not combined and experts were combined respectively.
Conclusion: The use of machine learning techniques can lead to the early diagnosis of COVID-19 and help physicians to accelerate the healing process. This study shows that a combination of deep experts leads to improved diagnosis accuracy.
|COVID-19؛ Deep Networks؛ Data Mining؛ Machine Learning؛ Mixture of Experts|
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