COVID-19 prediction with artificial intelligence based image processing methods

Aim: The aim of this study is to develop a high-performance model and web-based clinical decision making method to successfully distinguish and classify COVID-19 from Bacterial Pneumonia, Viral Pneumonia and healthy controls with Lung Ultrasound videos using appropriate video processing techniques and artificial intelligence methods. development of the support system. Material and Method: In this study, the open source Lung ultrasound video dataset at https://github.com/jannisborn/covid19_ultrasound was used. The dataset includes 32 healthy controls, 24 COVID-19, 24 Bacterial Pneumonia and 12 Viral Pneumonia class videos. In the video processing stage, 300 image frames were taken from the videos in each class. In this way, a total of 1200 images were obtained. 80% (960) of the images are divided into training datasets and 20% (240) as test datasets. In the modeling phase, the convolutional neural network (CNN) method, one of the deep neural network architectures in the keras library, was used. Accuracy, sensitivity, specificity, precision, Matthews' correlation coefficient (MCC), F1 score and G-ortalama criteria are given to evaluate the performance of the model. In addition to these, a web-based system has been developed that can successfully detect COVID-19 using the HTML5 infrastructure, with the help of the artificial intelligence-based model, Python Flask Library and JavaScript. Results: In this study, with the model created on the open access Lung ultrasound video dataset, the accuracy in the test dataset was calculated as 93.39% for healthy control, COVID-19 and viral pneumonia, and 95.07% for bacterial pneumonia. Conclusion: According to the performance criteria values obtained with the video processing-based CNN model, it can be said that the developed system gives very successful predictions in the diagnosis of COVID-19, Bacterial Pneumonia and Viral Pneumonia. Keywords: COVID-19, artificial intelligence, deep learning, video processing, image processing, convolutional neural networks.