Lung Segmentation Algorithm and SVM Classification of COVID-19 in CT Images

ABSTRACT

The analysis of COVID-19 by tomographic imaging has been a standard for pandemic management. The application of different types of artificial intelligence algorithms has proven to be an accurate method for disease detection. This study presents a method of lung segmentation and a classification algorithm that allows to discriminate between images that show signs of the disease and those that don’t. In addition, the article seeks to establish what kind of features are relevant when feeding a machine learning algorithm. Texture features extracted from Gray Label Concurrence Matrix (GLCM) and a Gabor filter are used for this purpose. Then, we trained and evaluated a SVM algorithm using different combinations of features. It is found that the features extracted from the Gabor filter work better than those extracted from the GLCM, finding that those features focused exclusively on intensity description work better than those focused on spatial description, at least in early stages. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.