Transfer learning-based lung segmentation and pneumonia detection for paediatric chest X-ray images. (7th March 2023)

• Record Type: Journal Article
• Authors: Fernandes, Vandecia; Bras, Gabriel; Paiva, Lisle Faray de; Junior, Geraldo Braz; Paiva, Anselmo Cardoso de; Rivero, Luis
• Is Part Of: International journal of innovative computing and applications. Volume 14:Number 1/2(2023)
• Page Start: 56
• Page End: 66
• Publication Date: 2023-03-07
• Subjects: pneumonia -- transfer learning -- deep learning -- segmentation; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

Pneumonia is the leading cause of morbidity and mortality in under-five children, especially in developing countries. Accordingly to UNICEF and World Health Organization, a child dies of pneumonia every 39 seconds, and pneumonia kills more children than any other infectious disease, accounting for 15% of all deaths of children under five years old. In regions with a high prevalence, the early detection and treatment of pneumonia can significantly reduce children's mortality rates. Commonly, a chest X-ray is a diagnostic exam. Nevertheless, it is a problematic image for reading and interprets, requiring an expert physician. So, it is essential to provide computational methods to help exam interpretation or enhance important information. This paper proposes a transfer learning method to segment lung regions on the chest X-ray dataset to extract ROI for pneumonia detection. The results are promising and reach 0.917 of dice using U-Net combined with InceptionV3 in a chest X-ray dataset without lung annotation. For pneumonia detection, the method achieves 0.954 precision.