Conference Proceedings

A Deep Transfer Learning Framework for Pneumonia Detection from Chest X-ray Images

Kh Tohidul Islam, Sudanthi Wijewickrema, Aaron Collins, Stephen O'Leary, GM Farinella (ed.), P Radeva (ed.), J Braz (ed.)

Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications | SCITEPRESS | Published : 2020

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 t..

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Grants

Funding Acknowledgements

This research was funded by the University of Melbourne through a Melbourne Research Scholarship (MRS) awarded to Kh Tohidul Islam in support of his Doctor of Philosophy degree. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Also, the authors would like to thank Dr. Jared Panario and Tayla Razmovski of the Department of Surgery (Otolaryngology), University of Melbourne, Victoria, Australia for their suggestions and clarifications.