A Novel Machine Learning Technique for Classification of COVID-19 and Pneumonia
Abstract
In order to control the spread of coronavirus and pneumonia, there is an increasing interest in the utilization of machine and deep learning frameworks for identification of the said diseases from x-ray images of patients. The main reason for this increasing interest is because the clinicians indicate that coronavirus and viral pneumonia must be diagnosed in the earliest stages. The research work presented in this paper is mainly based on machine learning and Artificial Neural Network techniques to identify the occurrence of coronavirus and pneumonia diseases from a dataset, which was established manually, comprising of x-ray images of patients. In this paper, machine learning and ANN algorithm have been employed along with different feature extraction techniques for KNN, SVM, and NB such as shape matching using Hu Moments, edge orientation, Haralick texture features. Subsequently augmentation technique was applied for data enhancement. To justify the effectiveness of the proposed method, a comparative analysis was performed with other established methods. The results exhibited that the proposed method has accuracy, precision, sensitivity, and F1 score of 94%, 0.94, 0.96 and 0.95 respectively.
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