Ear-Keeper: Real-time Diagnosis of Ear Lesions Utilizing Ultralight-Ultrafast ConvNet and Large-scale Ear Endoscopic Dataset

arXiv:2308.10610v4 Announce Type: replace
Abstract: Deep learning-based ear disease diagnosis technology has proven effective and affordable. However, due to the lack of ear endoscope datasets with diversity, the practical potential of the deep learning model has not been thoroughly studied. Moreover, existing research failed to achieve a good trade-off between model inference speed and parameter size, rendering models inapplicable in real-world settings. To address these challenges, we constructed the first large-scale ear endoscopic dataset comprising eight types of ear diseases …

abstract arxiv cs.cv cs.se dataset datasets deep learning diagnosis disease disease diagnosis diversity however practical real-time research scale technology type