Back to search

Article

OtoXNet - Automated Identification of Eardrum Diseases from Otoscope Videos: A Deep Learning Study for Video-representing Images

2021-08-07

Abstract excerpt

<h4>Background</h4> The lack of an objective method to evaluate the eardrum is a critical barrier to an accurate diagnosis. Eardrum images are classified into normal or abnormal categories with machine learning techniques. If the input is an otoscopy video, a traditional approach requires great effort and expertise to manually determine the representative frame(s). <h4>Methods</h4> In this paper, we propose a nove...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
1bc25e31-2f4c-59de-afa7-99103cf7fd18
DOI
10.1101/2021.08.05.21261672
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
OtoXNet - Automated Identification of Eardrum Diseases from Otoscope Videos: A Deep Learning Study for Video-representing ImagesDOI 10.1101/2021.08.05.21261672
Select a neighboring publication to make it the new centre.