Automatic tissue characterization of air trapping in chest radiographs using deep neural networks

Significant progress has been made in recent years for computer-aided diagnosis of abnormal pulmonary textures from computed tomography (CT) images. Similar initiatives in chest radiographs (CXR), the common modality for pulmonary diagnosis, are much less developed. CXR are fast, cost effective and...

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Bibliographic Details
Published in:2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2016; pp. 97 - 100
Main Authors: Mansoor, Awais, Perez, Geovanny, Nino, Gustavo, Linguraru, Marius George
Format: Conference Proceeding Journal Article
Language:English
Published: United States IEEE 01.08.2016
Subjects:
Air
Charge carrier processes
Computed tomography
Diagnosis, Computer-Assisted
Humans
Image Processing, Computer-Assisted - methods
Lung - diagnostic imaging
Lungs
Machine learning
Neural Networks, Computer
Radiography, Thoracic - methods
Shape
Training
Virus Diseases - diagnostic imaging
Visualization
ISSN:1557-170X, 2694-0604, 2694-0604
Online Access:Get full text
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Summary:Significant progress has been made in recent years for computer-aided diagnosis of abnormal pulmonary textures from computed tomography (CT) images. Similar initiatives in chest radiographs (CXR), the common modality for pulmonary diagnosis, are much less developed. CXR are fast, cost effective and low-radiation solution to diagnosis over CT. However, the subtlety of textures in CXR makes them hard to discern even by trained eye. We explore the performance of deep learning abnormal tissue characterization from CXR. Prior studies have used CT imaging to characterize air trapping in subjects with pulmonary disease; however, the use of CT in children is not recommended mainly due to concerns pertaining to radiation dosage. In this work, we present a stacked autoencoder (SAE) deep learning architecture for automated tissue characterization of air-trapping from CXR. To our best knowledge this is the first study applying deep learning framework for the specific problem on 51 CXRs, an F-score of ≈ 76.5% and a strong correlation with the expert visual scoring (R=0.93, p =<; 0.01) demonstrate the potential of the proposed method to characterization of air trapping.
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ISSN:1557-170X
2694-0604
2694-0604
DOI:10.1109/EMBC.2016.7590649