Original Fusing fine-tuned deep features for recognizing different tympanic membranes
Original Fusing fine-tuned deep features for recognizing different tympanic membranes
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DOI：
10.1016/j.bbe.2019.11.001
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发表时间：
2020-01-01
影响因子：
6.4
通讯作者：
Comert, Zafer
中科院分区：
文献类型：
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作者：
Comert, Zafer
中耳炎是一组与中耳有关的炎症性疾病。虽然有各种各样的疾病类型的OM，最常见的疾病是急性中耳炎（AOM），渗出性中耳炎（OME）和慢性化脓性中耳炎（CSOM）。临床上对OM的检查是主观实现的。这种主观检查容易出错，导致专家之间的差异有限。由于这些原因，需要计算机辅助系统。在这项研究中，我们专注于使用预先训练的深度卷积神经网络（DCNN）提供的融合微调深度特征来识别正常，AOM，CSOM和耳垢鼓膜（TM）条件。这些特征作为输入应用于几个网络，如人工神经网络（ANN），k-最近邻（k NN），决策树（DT）和支持向量机（SVM）。此外，我们发布了一个新的公开可用的TM数据集，共956耳镜图像。因此，DCNN产生了有希望的结果。特别是VGG-16提供了最有效的结果，准确率为93.05%。融合的微调深度特征提高了整体分类成功率。最后，所提出的模型产生了令人满意的结果，使用融合的微调深度特征和SVM模型的组合，准确率为99.47%，灵敏度为99.35%，特异性为99.77%。因此，这项研究表明，融合微调深功能是相当有用的，在识别不同的TM，这些功能可以提供一个完全自动化的模型，具有高灵敏度。(c)2019波兰科学院纳莱茨生物控制学和生物医学工程研究所。Elsevier B. V.出版，保留所有权利。
Otitis media (OM) refers to a group of inflammatory diseases regarding the middle ear. Although there are a wide variety of disease types regarding OM, the most commonly seen disorders are acute otitis media (AOM), otitis media with effusion (OME) and chronic suppurative otitis media (CSOM). The examination of OM in the clinics is realized subjectively. This subjective examination is error-prone and leads to a limited variability among specialist. For these reasons, computer-aided systems are in demand. In this study, we focus on recognizing normal, AOM, CSOM, and earwax tympanic membrane (TM) conditions using fused fine-tuned deep features provided by pre-trained deep convolutional neural networks (DCNNs). These features are applied as the input to several networks, such as an artificial neural network (ANN), k-nearest neighbor (k NN), decision tree (DT) and support vector machine (SVM). Moreover, we release a new publicly available TM data set consisting of totally 956 otoscope images. As a result, the DCNNs yielded promising results. Especially, the most efficient results were provided by VGG-16 with an accuracy of 93.05 %. The fused fine-tuned deep features improved the overall classification success. Finally, the proposed model yielded promising results with an accuracy of 99.47 %, sensitivity of 99.35 %, and specificity of 99.77 % using the combination of the fused fine-tuned deep features and SVM model. Consequently, this study shows that fused fine-tuned deep features are rather useful in recognizing different TMs and these features can provide a fully automated model with high sensitivity. (c) 2019 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.