Detecting pneumonia in chest radiographs using convolutional neural networks

Jennifer Ureta; Oya Aran; Joanna Pauline Rivera

doi:10.1117/12.2559527

31 January 2020 Detecting pneumonia in chest radiographs using convolutional neural networks

Jennifer Ureta, Oya Aran, Joanna Pauline Rivera

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114331Z (2020) https://doi.org/10.1117/12.2559527
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

Pneumonia is an infection of the lungs that can cause mild to severe illness and affects millions of people worldwide. Imaging studies are therefore crucial for the detection and management of patients with pneumonia, and radiography is currently the best method for diagnosis. However, clinical diagnosis of chest X-rays can be a challenging task as it requires interpretation by highly trained clinicians. This study uses deep learning to perform binary classification of frontal-view chest X-ray images to detect signs of childhood pneumonia. The effectiveness of the classifiers was validated using a dataset that was collected by [5] containing 5,856 labeled X-ray images from children. The classifiers were able to identify the presence or absence of childhood pneumonia with an accuracy between 96-97%.

Citation Download Citation

Jennifer Ureta, Oya Aran, and Joanna Pauline Rivera "Detecting pneumonia in chest radiographs using convolutional neural networks", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114331Z (31 January 2020); https://doi.org/10.1117/12.2559527

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