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

Amit Kumar Jaiswal
,
Prayag Tiwari
,
Sachin Kumar
,
Deepak Gupta
,
Ashish Khanna
,
Joel J.P.C. Rodrigues

Research Output: Contribution to journal Article Peer-review

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.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 511-518

Journal (Volume, Issue Number)

Measurement: Journal of the International Measurement Confederation (Volume 145)

Publication milestones

Accepted/In press - 21/05/2019
Published - 04/06/2019

Publication status

Published - 04/06/2019

ISSN

0263-2241

External Publication IDs

handle.net: 10547/623797
Scopus: 85067038629

Access to documents

10.1016/j.measurement.2019.05.076

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