Otitis media (OM), known as inflammation of the middle ear, is a condition especially seen in children. To carry out a definitive diagnosis of the discomfort that manifests itself with various symptoms such as pain in the ear, fever, and discharge, the eardrum in the middle ear should be examined by a specialist. In this study, a convolution neural network was used for feature extraction from middle ear otoscope images to diagnose different types of OM. These features were extracted using AlexNet, VGG-16, GoogLeNet, ResNet-50 models. The deep features extracted from these models were combined into a new deep feature vector. This feature vector consisting of 4000 deep features was examined, and the most relevant 222 deep features were selected from this large feature set by using the neighbourhood component analysis. In this case, the number of features was decreased and a more effective feature set was obtained. In the next stage of this experimental study, this new feature set was applied as the input to the support vector machine. As a result of the experimental study, an accuracy rate of 79.02 was achieved. The results point out that the use of deep features in detecting OM provides efficient results, and the proposed approach is beneficial in reducing the number of deep features as well as achieving better classification results.
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