Convolutional neural networks (CNNs), a popular type of deep neural network, have been actively applied to image recognition, object detection, object localization, semantic segmentation, and object instance segmentation. Accordingly, the applicability of deep learning to the analysis of medical images has increased. This paper presents a novel application of state-of-the-art CNN models, such as DenseNet, to the automatic detection of the tympanic membrane (TM) and middle ear (ME) infection. We collected 2,484 oto-endoscopic images (OEIs) and classified them into one of three categories: normal, chronic otitis media (COM) with TM perforation, and otitis media with effusion (OME). Our results indicate that CNN models have significant potential for the automatic recognition of TM and ME infections, demonstrating a competitive accuracy of 95% in classifying TM and middle ear effusion (MEE) from OEIs. In addition to accuracy measurement, our approach achieves nearly perfect measures of 0.99 in terms of the average area under the receiver operating characteristics curve (AUROC). All these results indicate robust performance when recognizing TM and ME effusions in OEIs. Visualization through a class activation mapping (CAM) heatmap demonstrates that our proposed model performs prediction based on the correct region of OEIs. All these outcomes ensure the reliability of our method; hence, the study can aid otolaryngologists and primary care physicians in real-world scenarios.

中文翻译：

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通过卷积神经网络从耳内窥镜图像自动检测鼓膜和中耳感染。

卷积神经网络（CNN）是一种流行的深度神经网络类型，已被积极地应用于图像识别，对象检测，对象定位，语义分割和对象实例分割。因此，深度学习在医学图像分析中的适用性增加了。本文介绍了最新的CNN模型（例如DenseNet）在鼓膜（TM）和中耳（ME）感染的自动检测中的新应用。我们收集了2,484张耳内窥镜图像（OEIs），并将它们分为三类之一：正常，带有TM穿孔的慢性中耳炎（COM）和带有渗出液（OME）的中耳炎。我们的结果表明，CNN模型具有自动识别TM和ME感染的巨大潜力，在对OEI的TM和中耳积液（MEE）进行分类时，证明其具有95％的竞争准确性。除了测量精度外，我们的方法还可以在接收器工作特性曲线（AUROC）下的平均面积上实现接近0.99的完美测量。所有这些结果表明，在识别OEI中的TM和ME渗出物时，性能稳定。通过类激活映射（CAM）热图的可视化表明，我们提出的模型基于OEI的正确区域执行预测。所有这些结果确保了我们方法的可靠性；因此，该研究可以在现实世界中为耳鼻喉科医生和初级保健医生提供帮助。接收器工作特性曲线（AUROC）下的平均面积为99。所有这些结果表明，在识别OEI中的TM和ME渗出物时，性能稳定。通过类激活映射（CAM）热图的可视化表明，我们提出的模型基于OEI的正确区域执行预测。所有这些结果确保了我们方法的可靠性；因此，该研究可以在现实世界中为耳鼻喉科医生和初级保健医生提供帮助。接收器工作特性曲线（AUROC）下的平均面积为99。所有这些结果表明，在识别OEI中的TM和ME渗出物时，性能稳定。通过类激活映射（CAM）热图的可视化表明，我们提出的模型基于OEI的正确区域执行预测。所有这些结果确保了我们方法的可靠性；因此，该研究可以在现实世界中为耳鼻喉科医生和初级保健医生提供帮助。