COVID-19诊断：基于CT和胸部X射线图像分类的ULGFBP-ResNet51方法
COVID-19 Diagnosis: ULGFBP-ResNet51 approach on the CT and the Chest X-ray Images Classification
- University of Tabriz（大不里士大学）
- Islamic Azad University（伊斯兰阿扎德大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出ULGFBP-ResNet51方法，结合ULBP、Gabor滤波器和ResNet51，用于CT和胸部X射线图像的COVID-19自动诊断，实现高准确率。
AI中文摘要：
具有传染性和大流行性的COVID-19疾病目前被视为主要的健康问题，并在人类中引发了广泛的恐慌。它严重影响人类呼吸道和肺部，因此对过早死亡构成了重大威胁。尽管早期诊断在康复阶段可以发挥关键作用，但带有手动干预的放射学检查是一个耗时的过程。在医院中，对大量患者进行这种手动检查的时间也有限。因此，对胸部X射线或CT图像进行高效性能的自动诊断的需求十分紧迫。为此，我们提出了一种新方法，命名为ULGFBP-ResNet51，以解决图像中的COVID-19诊断问题。实际上，该方法包括均匀局部二值模式（ULBP）、Gabor滤波器（GF）和ResNet51。根据我们的结果，与其他方法相比，该方法可以提供优越的性能，并获得最大准确率。
英文摘要：
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.
