Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks

COVID-19 represents one of the greatest challenges in modern history. Its impact is most noticeable in the health care system, mostly due to the accelerated and increased influx of patients with a more severe clinical picture. These facts are increasing the pressure on health systems. For this reason, the aim is to automate the process of diagnosis and treatment. The research presented in this article conducted an examination of the possibility of classifying the clinical picture of a patient using X-ray images and convolutional neural networks. The research was conducted on the dataset of 185 images that consists of four classes. Due to a lower amount of images, a data augmentation procedure was performed. In order to define the CNN architecture with highest classification performances, multiple CNNs were designed. Results show that the best classification performances can be achieved if ResNet152 is used. This CNN has achieved AUCmacro¯¯¯¯¯¯¯¯¯¯¯¯¯¯ and AUCmicro¯¯¯¯¯¯¯¯¯¯¯¯¯¯ up to 0.94, suggesting the possibility of applying CNN to the classification of the clinical picture of COVID-19 patients using an X-ray image of the lungs. When higher layers are frozen during the training procedure, higher AUCmacro¯¯¯¯¯¯¯¯¯¯¯¯¯¯ and AUCmicro¯¯¯¯¯¯¯¯¯¯¯¯¯¯ values are achieved. If ResNet152 is utilized, AUCmacro¯¯¯¯¯¯¯¯¯¯¯¯¯¯ and AUCmicro¯¯¯¯¯¯¯¯¯¯¯¯¯¯ values up to 0.96 are achieved if all layers except the last 12 are frozen during the training procedure.