Hybrid feature extraction technique for automatic classification of COVID-19 chest CT images

Abstract

ABSTRACT COVID-19 has seriously affected normal life as well as public safety. It is extremely transmissible and has now infected millions of people worldwide. To obtain more image features of the lungs, Computed Tomography (CT) scans are widely used. However, manual examination of CT images for abnormal areas of COVID-19 disease can be time-consuming, and it is highly subjective to determine whether they are infected. To rapidly screen patients, Machine Learning (ML) can be used to determine whether patients have the disease. In this paper, a hybrid extraction technique is used to extract feature vectors from CT images, which is a mixture of a histogram of orientation gradients (HOG) extraction technique and a local binary pattern (LBP) extraction technique. In this experiment, 960 NON-COVID-19 and 960 COVID-19 were adopted to train the model, and 240 NON-COVID-19 and 240 COVID-19 were used to test the model. And the CT images were scaled to a uniform size. After obtaining the feature vectors using HOG and LBP feature extraction methods, The CT images were classified using a Support Vector Machine (SVM) classifier optimised by Particle Swarm Optimisation (PSO). In the performance evaluation of the presented classification model, the combination of the HOG feature extraction technique and the LBP feature extraction technique resulted in a substantial improvement in the classification effectiveness of the SVM. HOG_LBP PSO SVM improved Accuracy to 97.5%, Precision to 97.75%, Recall to 97.27%, Specificity to 97.25%, F1_score to 97.50%, and Mcc to 95.01%.