Volume 20, Issue 3 (12-2023)                   JSDP 2023, 20(3): 27-46 | Back to browse issues page

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Mohammadian Takaloo V, Hashemzadeh M, Ghavidel Neycharan J. CoviX-Net: A Deep Learning-based System for Diagnosis and Differentiation of Covid-19 Infection and Pneumonia in Chest Radiography Images. JSDP 2023; 20 (3) : 3
URL: http://jsdp.rcisp.ac.ir/article-1-1238-en.html
Azarbaijan Shahid Madani University
Abstract:   (317 Views)
Coronavirus (Covid-19) is a new infectious disease with a very high rate of infection and mortality. Therefore, its early detection has become one of the vital measures of human society. This virus is commonly tested using sputum or blood samples, and the result is usually announced within hours or even days after the test. However, even an hour delay in announcing the test results can lead to many more people being infected. Another way to diagnose the virus is to take a chest X-ray (chest radiography images), which is much faster and cheaper than other tests. However, the rate of human diagnostic error from these images is high, and more importantly, it is very difficult to distinguish Covid-19 infection from other infections such as pneumonia. In this paper, an intelligent system, termed CoviX-Net, based on deep machine learning techniques, is presented to diagnose and differentiate Covid-19 disease and various types of pneumonia (bacterial or viral) using chest radiographs. The CoviX-Net learning model is based on the Xception architecture, the accuracy of which is improved by the use of transfer learning and data augmentation techniques. To provide adequate training data, a comprehensive database is created by integrating two different sources of chest X-ray images. The evaluations performed on the test images show that the accuracy of CoviX-Net diagnosis in the three-class mode (Covid-19, pneumonia, and normal lung) is %99.25, and in the four-class mode (Covid-19, bacterial pneumonia, viral pneumonia, and normal lung) is %95. Compared to other similar deep learning-based classification methods, the accuracy is improved by 5%, and compared to the transfer learning-based parallel deep learning method, with a complex structure, the accuracy is improved by about half a percent. These promising results demonstrate the superiority of CoviX-Net against the competitors, and suggest that CoviX-Net can be a useful tool to assist clinicians and radiologists in diagnosing patients with Covid-19. All the implementation source codes and collected dataset are made publicly available.
Article number: 3
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Type of Study: Research | Subject: Paper
Received: 2021/06/2 | Accepted: 2023/12/11 | Published: 2024/01/14 | ePublished: 2024/01/14

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