Classification of Tympanic Membrane Images Based on Deep Learning Model

This study investigates the application of Artificial Intelligence (AI) techniques, particularly Convolutional Neural Networks (CNNs), for the diagnosis of tympanic membrane diseases. A dataset comprising 956 tympanic membrane images was collected from various sources, representing conditions such as acute otitis media, chronic otitis media, foreign body presence, tympanosclerosis, and other prevalent disorders affecting the middle ear and tympanic membrane. The images were preprocessed and utilized for training and testing using deep learning methodologies. Several deep learning models were developed and evaluated on the dataset, with systematic optimization of hyperparameters, including the number of filters, filter sizes, and pooling strategies such as Max Pooling and Average Pooling, to enhance classification performance. The proposed models were assessed based on a range of metrics, and the optimal CNN-based model achieved an accuracy of 86.47% in classifying the diseases. The findings of this study underscore the potential of AI-driven solutions to improve medical diagnostics by offering reliable and efficient tools to assist healthcare professionals in the accurate identification of tympanic membrane diseases. Future work is recommended to explore advanced deep learning architectures and expand the dataset to further enhance accuracy and generalizability across diverse medical applications.