COVID-19 diagnosis: ULGFBP-ResNet51 approach on the CT and the chest X-ray images classiffcation

Document Type : Article

Authors

1 Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

2 Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

Abstract

The contagious and pandemic COVID-19 disease is currently considered as the main health concern and posed widespread panic across human-beings. It affects the human respiratory tract and lungs intensely. So that it has imposed significant threats for premature death. Although, its early diagnosis can play a vital role in revival phase, the radiography tests with the manual intervention are a time-consuming process. Time is also limited for such manual inspecting of numerous patients in the hospitals. Thus, the necessity of automatic diagnosis on the chest X-ray or the CT images with a high efficient performance is urgent. Toward this end, we propose a novel method, named as the ULGFBP-ResNet51 to tackle with the COVID-19 diagnosis in the images. In fact, this method includes Uniform Local Binary Pattern (ULBP), Gabor Filter (GF), and ResNet51. According to our results, this method could offer superior performance in comparison with the other methods, and attain maximum accuracy.

Keywords

Main Subjects

References

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Scientia Iranica
Volume 31, Issue 14
Transactions on Computer Science & Engineering and Electrical Engineering (D)
July and August 2024
Pages 1091-1104

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  • Receive Date: 07 July 2022
  • Revise Date: 03 January 2024
  • Accept Date: 12 May 2024

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