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    Identifying pneumonia in chest X-rays: a deep learning approach

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    Authors
    Jaiswal, Amit Kumar
    Tiwari, Prayag
    Kumar, Sachin
    Gupta, Deepak
    Khanna, Ashish
    Rodrigues, Joel J.P.C.
    Affiliation
    University of Bedfordshire
    University 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
    Subjects
    chest X-ray
    medical imaging
    object detection
    segmentation
    B800 Medical Technology
    
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    Abstract
    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
    Elsevier
    Journal
    Measurement
    URI
    http://hdl.handle.net/10547/623797
    DOI
    10.1016/j.measurement.2019.05.076
    Additional Links
    https://www.sciencedirect.com/science/article/pii/S0263224119305202
    Type
    Article
    Language
    en
    ISSN
    0263-2241
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.measurement.2019.05.076
    Scopus Count
    Collections
    Computing

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