Al-jumaili, SaifAl-azzawi, AtharDuru, Adil Deniz2022-02-042022-02-042021Al-Jumaili, S., Al-Azzawi, A., Duru, A. D., & Ibrahim, A. A. (2021, October). Covid-19 X-ray image classification using SVM based on Local Binary Pattern. In 2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (pp. 383-387). IEEE.https://hdl.handle.net/20.500.12939/2241Coronavirus 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.eninfo:eu-repo/semantics/closedAccessConvolutional Neural NetworksSVMLocal Binary Pattern (LBP)COVID-19Deep LearningX-Ray ImageArticle3833872-s2.0-85123319534N/A