Computer-aided pulmonary disease diagnosis using lung ultrasound video

The development of a machine learning-based computer-aided diagnosis (CAD) system implemented for processing lung ultrasound images will greatly assist doctors in making decisions in diagnosing lung diseases. The learning method of the classifier model used in the computer-aided diagnosis system will affect the system's accuracy in diagnosing lung disease. Determining variables in the classifier and image pre-processing stages requires special attention to obtain a highly accurate classifier model. This study presents the development of a machine learning-based CAD as an add-on tool to classify lung disease based on a lung ultrasound (LUS) video. The main steps in this study are capturing the LUS videos and converting them into images, image pre-processing for speckle noise removal, image contrast and brightness enhancement, feature extraction, and the classification stage. In this study, three learning algorithm models, namely Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Naïve Bayes (NB), were used to classify images into three categories, namely healthy conditions, pneumonia, and COVID-19. The performance of the three classifier models is compared to each other to obtain the best classifier model. The experimental results demonstrate the superiority of the suggested strategy utilizing the SVM classifier. Based on experimental data using 2,149 lung images for three classes and 20 texture feature sets, the SVM has an accuracy of 98.1%, the KNN is 94.7%, and the Gaussian NB is 79.6%. The model with the highest accuracy will be used to develop the computer-aided diagnosis (CAD) system.