Background Otitis media (OM) is the infection and inflammation of the mucous membrane covering the Eustachian with the airy cavities of the middle ear and temporal bone. OM is also one of the most common ailments. In clinical practice, the diagnosis of OM is carried out by visual inspection of otoscope images. This vulnerable process is subjective and error-prone. Methods In this study, a novel computer-aided decision support model based on the convolutional neural network (CNN) has been developed. To improve the generalized ability of the proposed model, a combination of the channel and spatial model (CBAM), residual blocks, and hypercolumn technique is embedded into the proposed model. All experiments were performed on an open-access tympanic membrane dataset that consists of 956 otoscopes images collected into five classes. Results The proposed model yielded satisfactory classification achievement. The model ensured an overall accuracy of 98.26%, sensitivity of 97.68%, and specificity of 99.30%. The proposed model produced rather superior results compared to the pre-trained CNNs such as AlexNet, VGG-Nets, GoogLeNet, and ResNets. Consequently, this study points out that the CNN model equipped with the advanced image processing techniques is useful for OM diagnosis. The proposed model may help to field specialists in achieving objective and repeatable results, decreasing misdiagnosis rate, and supporting the decision-making processes.

中文翻译：

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使用鼓膜图像和新型多类机器学习算法检测中耳炎

背景中耳炎 (OM) 是中耳和颞骨的通风腔覆盖咽鼓管的粘膜的感染和炎症。OM也是最常见的疾病之一。在临床实践中，OM的诊断是通过耳镜图像的目视检查来进行的。这个易受攻击的过程是主观的并且容易出错。方法 在本研究中，开发了一种基于卷积神经网络 (CNN) 的新型计算机辅助决策支持模型。为了提高所提出模型的泛化能力，将通道和空间模型（CBAM）、残差块和超列技术的组合嵌入到所提出的模型中。所有实验都是在一个开放访问的鼓膜数据集上进行的，该数据集由 956 个耳镜图像组成，收集到五个类别中。结果 提出的模型取得了令人满意的分类效果。该模型保证了98.26%的总体准确率、97.68%的敏感性和99.30%的特异性。与 AlexNet、VGG-Nets、GoogLeNet 和 ResNets 等预训练的 CNN 相比，所提出的模型产生了相当出色的结果。因此，本研究指出，配备先进图像处理技术的 CNN 模型可用于 OM 诊断。所提出的模型可以帮助现场专家实现客观和可重复的结果，降低误诊率，并支持决策过程。与 AlexNet、VGG-Nets、GoogLeNet 和 ResNets 等预训练的 CNN 相比，所提出的模型产生了相当出色的结果。因此，本研究指出，配备先进图像处理技术的 CNN 模型可用于 OM 诊断。所提出的模型可以帮助现场专家实现客观和可重复的结果，降低误诊率，并支持决策过程。与 AlexNet、VGG-Nets、GoogLeNet 和 ResNets 等预训练的 CNN 相比，所提出的模型产生了相当出色的结果。因此，本研究指出，配备先进图像处理技术的 CNN 模型可用于 OM 诊断。所提出的模型可以帮助现场专家实现客观和可重复的结果，降低误诊率，并支持决策过程。