Traditional ear disease diagnosis heavily depends on experienced specialists and specialized equipment, frequently resulting in misdiagnoses, treatment delays, and financial burdens for some patients. Utilizing deep learning models for efficient ear disease diagnosis has proven effective and affordable. However, existing research overlooked model inference speed and parameter size required for deployment. To tackle these challenges, we constructed a large-scale dataset comprising eight ear disease categories and normal ear canal samples from two hospitals. Inspired by ShuffleNetV2, we developed Best-EarNet, an ultrafast and ultralight network enabling real-time ear disease diagnosis. Best-EarNet incorporates the novel Local-Global Spatial Feature Fusion Module which can capture global and local spatial information simultaneously and guide the network to focus on crucial regions within feature maps at various levels, mitigating low accuracy issues. Moreover, our network uses multiple auxiliary classification heads for efficient parameter optimization. With 0.77M parameters, Best-EarNet achieves an average frames per second of 80 on CPU. Employing transfer learning and five-fold cross-validation with 22,581 images from Hospital-1, the model achieves an impressive 95.23% accuracy. External testing on 1,652 images from Hospital-2 validates its performance, yielding 92.14% accuracy. Compared to state-of-the-art networks, Best-EarNet establishes a new state-of-the-art (SOTA) in practical applications. Most importantly, we developed an intelligent diagnosis system called Ear Keeper, which can be deployed on common electronic devices. By manipulating a compact electronic otoscope, users can perform comprehensive scanning and diagnosis of the ear canal using real-time video. This study provides a novel paradigm for ear endoscopy and other medical endoscopic image recognition applications.

翻译：传统耳病诊断严重依赖经验丰富的专家和专用设备，常导致误诊、治疗延误及患者经济负担。利用深度学习模型进行高效耳病诊断已被证实有效且经济。然而现有研究忽视了模型推理速度和部署所需的参数量。为解决这些挑战，我们构建了一个包含八类耳病类别及正常耳道样本的大规模数据集，数据来自两家医院。受ShuffleNetV2启发，我们开发了Best-EarNet——一种实现实时耳病诊断的超快超轻网络。Best-EarNet引入创新的局部-全局空间特征融合模块，可同步捕获全局与局部空间信息，引导网络聚焦不同层级特征图中的关键区域，缓解低精度问题。此外，本网络采用多个辅助分类头实现高效参数优化。Best-EarNet在仅0.77M参数下，CPU帧率可达80 FPS。基于医院-1的22,581张图像，采用迁移学习与五折交叉验证，模型准确率达95.23%。医院-2的1,652张图像外部测试验证其性能，准确率92.14%。与现有最优网络相比，Best-EarNet在实践应用中创建了新的最先进性能。最重要的是，我们开发了名为Ear Keeper的智能诊断系统，可部署于普通电子设备。用户通过操控紧凑型电子耳镜，即可利用实时视频对耳道进行全面扫描诊断。本研究为耳内窥镜及其他医用内窥镜图像识别应用提供了新范式。