Title: Transfer learning-based lung segmentation and pneumonia detection for paediatric chest X-ray images

Authors: Vandecia Fernandes; Gabriel Bras; Lisle Faray de Paiva; Geraldo Braz Junior; Anselmo Cardoso de Paiva; Luis Rivero

Addresses: Applied Computer Group NCA-UFMA, Federal University of Maranhão, Sao Luis, MA, Brazil ' Applied Computer Group NCA-UFMA, Federal University of Maranhão, Sao Luis, MA, Brazil ' Applied Computer Group NCA-UFMA, Federal University of Maranhão, Sao Luis, MA, Brazil ' Applied Computer Group NCA-UFMA, Federal University of Maranhão, Sao Luis, MA, Brazil ' Applied Computer Group NCA-UFMA, Federal University of Maranhão, Sao Luis, MA, Brazil ' Applied Computer Group NCA-UFMA, Federal University of Maranhão, Sao Luis, MA, Brazil

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.

Keywords: pneumonia; transfer learning; deep learning; segmentation.

DOI: 10.1504/IJICA.2023.129358

International Journal of Innovative Computing and Applications, 2023 Vol.14 No.1/2, pp.56 - 66

Received: 10 Sep 2020
Accepted: 16 Mar 2021
Published online: 07 Mar 2023 *

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