基于局部二值模式的SVM新冠肺炎x射线图像分类

Saif Al-jumaili, Athar Al-azzawi, A. Duru, A. Ibrahim
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引用次数: 6

摘要

冠状病毒通常由动物传播给人类,但现在,病毒在人与人之间传播。因此,科学家和研究人员正在努力开发几种类型的机器学习方法来防御COVID-19。医学图像在这一时期发挥着重要作用,因为它们可以用来准确识别COVID-19。然而,在本文中,我们使用x射线图像,图像经过锐化技术,以进一步增加结果。采用局部二值模式(LBP)纹理技术提取特征。将得到的特征应用于支持向量机(SVM)。在所有性能测量中,我们达到了100%的结果。我们的研究结果明显优于已发表的最先进的论文。
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Covid-19 X-ray image classification using SVM based on Local Binary Pattern
Coronavirus usually transmits from the animal to the human, but now, the virus transmission is between persons. Therefore, scientists and researchers are trying to develop several types of machine learning methods to defend against COVID-19. Medical images play a significant role in this time due to they can be used to recognize COVID-19 accurately. However, in this paper, we used X-Ray images, the images undergone to sharpening techniques to increase the results further. The texture techniques named local binary pattern (LBP) have been used in order to extract features. The features obtained were applied to the support vector machine (SVM). The results we achieved were 100% for all performance measurements. Our results were conspicuously superior compared to the state-of-the-art papers published.
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