Automated multi-class classification for prediction of tympanic membrane changes with deep learning models

ABSTRACT:

Backgrounds and objective

Evaluating the tympanic membrane (TM) using an otoendoscope is the first and most important step in various clinical fields. Unfortunately, most lesions of TM have more than one diagnostic name. Therefore, we built a database of otoendoscopic images with multiple diseases and investigated the impact of concurrent diseases on the classification performance of deep learning networks.

Study design

This retrospective study investigated the impact of concurrent diseases in the tympanic membrane on diagnostic performance using multi-class classification. A customized architecture of EfficientNet-B4 was introduced to predict the primary class (otitis media with effusion (OME), chronic otitis media (COM), and ’None’ without OME and COM) and secondary cla

SUBMITTER: Choi Y 

PROVIDER: S-EPMC9550050 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature