Transfer learning-based lung segmentation and pneumonia detection for paediatric chest X-ray images
by Vandecia Fernandes; Gabriel Bras; Lisle Faray de Paiva; Geraldo Braz Junior; Anselmo Cardoso de Paiva; Luis Rivero
International Journal of Innovative Computing and Applications (IJICA), Vol. 14, No. 1/2, 2023

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.

Online publication date: Tue, 07-Mar-2023

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