Multi-stage transfer learning for lung segmentation using portable X-ray devices for patients with COVID-19. (1st July 2021)

• Record Type: Journal Article
• Authors: Vidal, Plácido L.; de Moura, Joaquim; Novo, Jorge; Ortega, Marcos
• Is Part Of: Expert systems with applications. Volume 173(2021)
• Publication Date: 2021-07-01
• Subjects: CAD system -- Radiography -- X-ray -- Lung segmentation -- COVID-19 -- Transfer learning; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.114677
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Multiple transfer learning methodology able to work with a limited number of samples. Fully automatic proposal to segment the pulmonary region in low quality radiographs. Only fully automatic methodology designed to work with portable X-ray devices. Portable devices help to prevent contagion, and are the recommended during pandemic. Critical during the COVID-19 pandemic, as these devices return poorer quality images. Abstract: One of the main challenges in times of sanitary emergency is to quickly develop computer aided diagnosis systems with a limited number of available samples due to the novelty, complexity of the case and the urgency of its implementation. This is the case during the current pandemic of COVID-19. This pathogen primarily infects the respiratory system of the afflicted, resulting in pneumonia and in a severe case of acute respiratory distress syndrome. This results in the formation of different pathological structures in the lungs that can be detected by the use of chest X-rays. Due to the overload of the health services, portable X-ray devices are recommended during the pandemic, preventing the spread of the disease. However, these devices entail different complications (such as capture quality) that, together with the subjectivity of the clinician, make the diagnostic process more difficult and suggest the necessity for computer-aided diagnosis methodologies despite the scarcity of samples available to do so. To solve this problem, we propose a methodology that allows to adapt the knowledge from a well-known domain with a high number of samples to a new domain with a significantly reduced number and greater complexity. We took advantage of a pre-trained segmentation model from brain magnetic resonance imaging of a unrelated pathology and performed two stages of knowledge transfer to obtain a robust system able to segment lung regions from portable X-ray devices despite the scarcity of samples and lesser quality. This way, our methodology obtained a satisfactory accuracy of 0.9761 ± 0.0100 for patients with COVID-19, 0.9801 ± 0.0104 for normal patients and 0.9769 ± 0.0111 for patients with pulmonary diseases with similar characteristics as COVID-19 (such as pneumonia) but not genuine COVID-19.