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