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Fusing fine-tuned deep features for recognizing different tympanic membranes

Cömert Zafer

Biocybernetics and Biomedical Engineering

2020

Vol. 40, no. 1

40--51

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 subjec-tively. 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.

Influence of middle ear disorder in round-window stimulation using a finite element human ear model

Zhou Kai, Liu Houguang, Yang Jianhua, Zhao Yu, Rao Zhushi, Yang Shanguo

Acta of Bioengineering and Biomechanics

2019

Vol. 21, no. 1

3--12

The aim of this work was to study the effect of middle ear disorder on round window (RW) stimulation, so as to provide references for the optimal design of RW stimulation type middle ear implants (MEIs). Methods: A human ear finite-element model was built by reverse engineering technique based on micro-computed tomography scanning images of human temporal bone, and was validated by three sets of comparisons with experimental data. Then, based on this model, typical disorders in otosclerosis and otitis media were simulated. Finally, their influences on the RW stimulation were analyzed by comparison of the displacements of the basilar membrane. Results: For the otosclerosis, the stapedial abnormal bone growth severely deteriorated the equivalent sound pressure of the RW stimulation at higher frequencies, while the hardening of ligaments and tendons prominently decreased the RW stimulation at lower frequencies. Besides, among the hardening of the studied tissues, the influence of the stapedial annular ligament’s hardening was much more significant. For the otitis media, the round window membrane (RWM)’s thickening mainly decreased the RW stimulation’s performance at lower frequencies. When the elastic modulus’ reduction of the RWM was considered at the same time especially for the acute otitis media, it would raise the lower-frequency performance of the RW stimulation. Conclusions: The influence of the middle ear disorder on the RW stimulation is considerable and variable, it should be considered during the design of the RW stimulation type MEIs.