Toward automated severe pharyngitis detection with smartphone camera using deep learning networks

Published: 30 July 2020| Version 2 | DOI: 10.17632/8ynyhnj2kz.2
Contributor:
TaeKeun Yoo

Description

Here we present a deep learning model with smartphone-based throat images facilitating detection of severe pharyngitis in telemedicine settings. We collected throat images from the web-based open social Q&A systems including Naver Korea (https://kin.naver.com), Yahoo Japan (https://chiebukuro.yahoo.co.jp). The additional throat image datasets were extracted using the Google image search engine. The search strategy was based on the key terms “sore throat”, “pharyngitis”, “tonsillitis”, “exudative tonsillitis”, “tonsillopharyngitis”, “throat image”, and “smartphone” in Korean, Japanese, and English. The most updated electronic database search was on June 30, 2020. We manually excluded throat images which were not acquired using smartphone. The images with the characteristics of the pharyngitis were manually classified by two clinicians, and the ambiguous images were isolated to clarify the image domains. Finally, we collected the initial dataset with a total of two classes including 147 throat images with pharyngitis and 215 normal throat images.

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