Identification of multiclass tympanic membranes by using deep feature transfer learning and hyperparameter optimization

•A high-performance and efficient system for detect type of otitis media.•Hyper parameter optimization of deep learning model.•Proposed model can assist the otolaryngologist to make accurate diagnosis.•A novel deep learning model for classification of tympanic membrane conditions. Middle ear health is a process that generally depends on eardrum health. Middle ear disorders are more common during childhood. Permanent damage may occur in bacterial or viral infections in this region if an early diagnosis is not made. Infectious ear disease, especially known as Otitis Media, is one of the diseases. In this study, morphological features of images are obtained by using various feature extraction methods. Deep feature-based transfer learning and hyperparameter optimization methods were used to detect the presence and type of otitis media. While the EfficientNet convolutional neural network (CNN) model was used to extract deep features, KNN, SVM, and Ensemble classifiers were used as classifiers. Bayesian, Grid Search, and Random Search were used for hyperparameter optimization. As a result of the experiments carried out, it was observed that the classification performance was 99.1%.