Alhudhaif Adi, Cömert Zafer, Polat Kemal
Department of Computer Science, College of Computer Engineering and Sciences in Al-kharj, Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia.
Department of Software Engineering, Samsun University, Samsun, Turkey.
PeerJ Comput Sci. 2021 Feb 23;7:e405. doi: 10.7717/peerj-cs.405. eCollection 2021.
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.
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.
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.
中耳炎（OM）是覆盖咽鼓管以及中耳和颞骨气腔的黏膜发生的感染和炎症。中耳炎也是最常见的疾病之一。在临床实践中，中耳炎的诊断是通过耳镜图像的目视检查来进行的。这个易受影响的过程具有主观性且容易出错。
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）、残差块和超柱技术相结合嵌入到所提出的模型中。所有实验均在一个开放获取的鼓膜数据集上进行，该数据集由956张耳镜图像组成，分为五类。
所提出的模型取得了令人满意的分类成果。该模型的总体准确率为98.26%，灵敏度为97.68%，特异性为99.30%。与预训练的CNN（如AlexNet、VGG-Nets、GoogLeNet和ResNets）相比，所提出的模型产生了相当优越的结果。因此，本研究指出配备先进图像处理技术的CNN模型对中耳炎诊断是有用的。所提出的模型可能有助于现场专家获得客观且可重复的结果，降低误诊率，并支持决策过程。
