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Authors
Jaiswal, Amit KumarTiwari, Prayag
Kumar, Sachin
Gupta, Deepak
Khanna, Ashish
Rodrigues, Joel J.P.C.
Affiliation
University of BedfordshireUniversity of Padova
South Ural State University
Maharaja Agrasen Institute of Technology
National Institute of Telecommunications (Inatel), Brazil
Instituto de Telecomunicações, Portugal
Federal University of Piauí, Brazil
Issue Date
2019-06-04
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Show full item recordAbstract
The rich collection of annotated datasets piloted the robustness of deep learning techniques to effectuate the implementation of diverse medical imaging tasks. Over 15% of deaths include children under age five are caused by pneumonia globally. In this study, we describe our deep learning based approach for the identification and localization of pneumonia in Chest X-rays (CXRs) images. Researchers usually employ CXRs for the diagnostic imaging study. Several factors such as positioning of the patient and depth of inspiration can change the appearance of the chest X-ray, complicating interpretation further. Our identification model (https://github.com/amitkumarj441/identify_pneumonia) is based on Mask-RCNN, a deep neural network which incorporates global and local features for pixel-wise segmentation. Our approach achieves robustness through critical modifications of the training process and a novel post-processing step which merges bounding boxes from multiple models. The proposed identification model achieves better performances evaluated on chest radiograph dataset which depict potential pneumonia causes.Citation
Jaiswal AK, Tiwari P, Kumar S, Gupta D, Khanna A, Rodrigues JJ (2019) 'Identifying pneumonia in chest X-rays: a deep learning approach', Measurement, 145 (), pp.511-518.Publisher
ElsevierJournal
MeasurementAdditional Links
https://www.sciencedirect.com/science/article/pii/S0263224119305202Type
ArticleLanguage
enISSN
0263-2241ae974a485f413a2113503eed53cd6c53
10.1016/j.measurement.2019.05.076