Detecting pneumonia in chest radiographs using convolutional neural networks
College
College of Computer Studies
Department/Unit
Software Technology
Document Type
Conference Proceeding
Source Title
Proceedings of SPIE - The International Society for Optical Engineering
Volume
11433
Publication Date
1-1-2020
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%. © 2020 SPIE.
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Digitial Object Identifier (DOI)
10.1117/12.2559527
Recommended Citation
Ureta, J. C., Aran, O., & Rivera, J. (2020). Detecting pneumonia in chest radiographs using convolutional neural networks. Proceedings of SPIE - The International Society for Optical Engineering, 11433 https://doi.org/10.1117/12.2559527
Disciplines
Computer Sciences | Software Engineering
Keywords
Chest—Radiography; Pneumonia—Imaging; Image processing
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