| Autor: |
Esmaeili, V., Feghhi, M. Mohassel, Shahdi, S. O. |
| Předmět: |
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| Zdroj: |
Scientia Iranica. Transaction D, Computer Science & Engineering & Electrical Engineering; Jul/Aug2024, Vol. 31 Issue 14, p1091-1104, 14p |
| Abstrakt: |
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 Computed Tomography (CT) images with a high effcient 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 51-layer Residual Neural Networks (ResNet51). According to our results, this method could offer superior performance in comparison with the other methods, and attain maximum accuracy. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Supplemental Index |
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