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COVID-19 is a pandemic disease affecting the global mankind since December 2019 Diagnosing COVID-19 using lung X-ray image is a great challenge faced by the entire world Early detection helps the doctors to suggest suitable treatment and also helps speedy recovery of the patients Advancements in the field of computer vision aid medical practitioners to predict and diagnosis disease accurately Objective: This study aims to analyze the chest X-ray for determining the presence of COVID-19 using machine learning algo-rithm Methods: Researchers propose various techniques using machine learning algorithms and deep learning approaches to de-tect COVID-19 However, obtaining an accurate solution using these AI techniques is the main challenge still remains open to researchers Results: This paper proposes a Local Binary Pattern technique to extract discriminant features for distinguishing COVID-19 disease using the X-ray images The extracted features are given as input to various classifiers namely Random Forest (RF), Linear Discriminant Analysis (LDA), k-Nearest Neighbour (kNN), Classification and Regression Trees (CART), Support Vector Machine (SVM), Linear Regression (LR), and Multi-layer perceptron neural network (MLP) The proposed model has achieved an accuracy of 77 7% from Local Binary Pattern (LBP) features coupled with Random Forest classifier Conclusion: The proposed algorithm analyzed COVID X-ray images to classify the data in to COVID-19 or not The features are extracted and are classified using machine learning algorithms The model achieved high accuracy for linear binary pattern with random forest classifier © IJCRR

Diagnosis of COVID-19 using Optimized PCA based Local Binary Pattern Features