Diagnosis of Tympanic Membrane Disease and Pediatric Hearing Using Convolutional Neural Network Models with Multi-Layer Perceptrons

Abstract

In this study, we propose a method of classification for tympanic membrane diseases and regression of pediatric hearing, using a deep learning model of artificial neural networks. Based on the B7 Backbone model of EfficientNet, a state-of-the-art convolutional neural network model, drop connect was applied in the encoder for generalization, and multi-layer perceptron, which is mainly used in the transformer, was applied to the decoder for improved accuracy. For the training data, the open-access tympanic membrane dataset, divided into four classes, was used as the benchmark dataset, and the SCH tympanic membrane dataset with five classes of tympanic membrane diseases and pediatric hearing was also used as the training dataset. In the benchmark using the open-access tympanic membrane dataset, the proposed model showed the highest performance among the five comparative models with an average accuracy of 93.59%, an average sensitivity of 87.19%, and an average specificity of 95.73%. In the experiment trained on the SCH tympanic membrane disease dataset, the average accuracy was 98.28%, the average sensitivity was 89.66%, the average specificity was 98.68%, and the average inference time was 0.2 s. In the experiment trained on the SCH pediatric hearing dataset, the mean absolute error was 6.8678, the mean squared logarithmic error was 0.2887, and the average inference time was 0.2 s.