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Authors: Kh Islam ; Sudanthi Wijewickrema ; Aaron Collins and Stephen O’Leary

Affiliation: Department of Surgery (Otolaryngology), Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Victoria 3010, Australia

ISBN: 978-989-758-402-2

ISSN: 2184-4321

Keyword(s): Pneumonia Detection using X-ray Images, Deep Learning, Transfer Learning, Feature Extraction, Artificial Neural Networks.

Abstract: Pneumonia occurs when the lungs are infected by a bacterial, viral, or fungal infection. Globally, it is the largest solo infectious disease causing child mortality. Early diagnosis and treatment of this disease are critical to avoid death, especially in infants. Traditionally, pneumonia diagnosis was performed by expert radiologists and/or doctors by analysing X-ray images of the chest. Automated diagnostic methods have been developed in recent years as an alternative to expert diagnosis. Deep learning-based image processing has been shown to be effective in automated diagnosis of pneumonia. However, deep leaning typically requires a large number of labelled samples for training, which is time consuming and expensive to obtain in medical applications as it requires the input of human experts. Transfer learning, where a model pretrained for a task on an existing labelled database is adapted to be reused for a different but related task, is a common workaround to this issue. Here, we explore the use of deep transfer learning to diagnose pneumonia using X-ray images of the chest. We demonstrate that using two individual pretrained models as feature extractors and training an artificial neural network on these features is an effective way to diagnose pneumonia. We also show through experiments that the proposed method outperforms similar existing methods with respect to accuracy and time. (More)

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Paper citation in several formats:
Islam, K.; Wijewickrema, S.; Collins, A. and O’Leary, S. (2020). A Deep Transfer Learning Framework for Pneumonia Detection from Chest X-ray Images. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-402-2 ISSN 2184-4321, pages 286-293. DOI: 10.5220/0008927002860293

@conference{visapp20,
author={Kh Islam. and Sudanthi Wijewickrema. and Aaron Collins. and Stephen O’Leary.},
title={A Deep Transfer Learning Framework for Pneumonia Detection from Chest X-ray Images},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2020},
pages={286-293},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008927002860293},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - A Deep Transfer Learning Framework for Pneumonia Detection from Chest X-ray Images
SN - 978-989-758-402-2
IS - 2184-4321
AU - Islam, K.
AU - Wijewickrema, S.
AU - Collins, A.
AU - O’Leary, S.
PY - 2020
SP - 286
EP - 293
DO - 10.5220/0008927002860293

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