An XAI approach for COVID-19 detection using transfer learning with X-ray images. Issue 4 (April 2023)

• Record Type: Journal Article
• Authors: Sarp, Salih; Catak, Ferhat Ozgur; Kuzlu, Murat; Cali, Umit; Kusetogullari, Huseyin; Zhao, Yanxiao; Ates, Gungor; Guler, Ozgur
• Is Part Of: Heliyon. Volume 9:Issue 4(2023)
• Publication Date: 2023-04
• Subjects: COVID-19 -- Explainable artificial intelligence -- Transfer learning; Research -- Periodicals; Medical sciences -- Periodicals; Natural history -- Periodicals; Social sciences -- Periodicals; Earth sciences -- Periodicals; Physical sciences -- Periodicals; 507.2
• DOI: 10.1016/j.heliyon.2023.e15137
• Languages: English
• ISSNs: 2405-8440

Abstract

Abstract: The coronavirus disease (COVID-19) has continued to cause severe challenges during this unprecedented time, affecting every part of daily life in terms of health, economics, and social development. There is an increasing demand for chest X-ray (CXR) scans, as pneumonia is the primary and vital complication of COVID-19. CXR is widely used as a screening tool for lung-related diseases due to its simple and relatively inexpensive application. However, these scans require expert radiologists to interpret the results for clinical decisions, i.e., diagnosis, treatment, and prognosis. The digitalization of various sectors, including healthcare, has accelerated during the pandemic, with the use and importance of Artificial Intelligence (AI) dramatically increasing. This paper proposes a model using an Explainable Artificial Intelligence (XAI) technique to detect and interpret COVID-19 positive CXR images. We further analyze the impact of COVID-19 positive CXR images using heatmaps. The proposed model leverages transfer learning and data augmentation techniques for faster and more adequate model training. Lung segmentation is applied to enhance the model performance further. We conducted a pre-trained network comparison with the highest classification performance (F1-Score: 98%) using the ResNet model.