  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 and
disease-free samples from two institutions. Inspired by ShuffleNetV2, we
proposed Best-EarNet, an ultrafast and ultralight network enabling real-time
ear disease diagnosis. Best-EarNet incorporates a novel Local-Global Spatial
Feature Fusion Module and multi-scale supervision strategy, which facilitates
the model focusing on global-local information within feature maps at various
levels. Utilizing transfer learning, the accuracy of Best-EarNet with only
0.77M parameters achieves 95.23% (internal 22,581 images) and 92.14% (external
1,652 images), respectively. In particular, it achieves an average frame per
second of 80 on the CPU. From the perspective of model practicality, the
proposed Best-EarNet is superior to state-of-the-art backbone models in ear
lesion detection tasks. Most importantly, Ear-keeper, an intelligent diagnosis
system based Best-EarNet, was developed successfully and deployed on common
electronic devices (smartphone, tablet computer and personal computer). In the
future, Ear-Keeper has the potential to assist the public and healthcare
providers in performing comprehensive scanning and diagnosis of the ear canal
in real-time video, thereby promptly detecting ear lesions.
