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A Mobile Application for Early Diagnosis of Pneumonia in the Rural context

Sait, U and Shivakumar, S and Gokul Lal, KV and Kumar, T and Ravishankar, VD and Bhalla, K (2019) A Mobile Application for Early Diagnosis of Pneumonia in the Rural context. In: 2019 IEEE Global Humanitarian Technology Conference (GHTC), 17-20 Oct. 2019, Seattle, WA, USA, USA.

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Official URL: https://dx.doi.org/10.1109/GHTC46095.2019.9033048


Pneumonia is a respiratory infection resulting in inflammation of the lungs. The causes of this infectious disease could be attributed to viruses, bacteria or fungi. One of the many ways of detecting the disease is by a chest X-ray of the patient. The rural population in developing nations have limited access to doctors, medical diagnostic facilities, and hospitals. Hence, diagnosis is delayed resulting in adverse consequences. This paper is an attempt to design and develop a smartphone-based application (app) for the preliminary detection of pneumonia using X-ray images. The app is based on machine learning which identifies pneumonia, using a chest X-ray image of a patient with a 'MobileNets' model, trained on thousands of X-ray images of known cases of pneumonia. The app has been developed on Android Studio, incorporating TensorFlow library. The patient's chest X-ray is scanned and uploaded to the app using the smartphone camera. Additionally, an e-diagnosis facility is integrated into the app where qualified medical practitioners' advice is taken on the obtained results. A breathing pattern recorder module is developed, which, in future, could be integrated into the smartphone app to increase its accuracy in prediction.

Item Type: Conference Paper
Publication: 2019 IEEE Global Humanitarian Technology Conference, GHTC 2019
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: cited By 0; Conference of 9th Annual IEEE Global Humanitarian Technology Conference, GHTC 2019 ; Conference Date: 17 October 2019 Through 20 October 2019; Conference Code:158424
Keywords: Computer viruses; E-learning; Learning systems; Machine learning; mHealth; Pathology; Smartphones; Viruses; X rays, Breathing patterns; Medical diagnostics; Medical practitioner; Mobile applications; MobileNets; pneumonia; Smart-phone cameras; TensorFlow, Diagnosis
Department/Centre: Division of Mechanical Sciences > Centre for Product Design & Manufacturing
Date Deposited: 21 Sep 2020 06:57
Last Modified: 21 Sep 2020 06:57
URI: http://eprints.iisc.ac.in/id/eprint/65138

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