Development of ResNet152 UNet++-Based Segmentation Algorithm for the Tympanic Membrane and Affected Areas

Abstract

Otitis media (OM) is a common disease in childhood that may have aftereffects such as hearing loss. Therefore, early diagnosis and proper treatment are important. However, the diagnostic accuracies of otolaryngology and pediatrics are low, at 73% and 50%, respectively. Therefore, clinical work that supports the early diagnosis of diseases, such as computer-aided diagnostic (CAD) systems, can be helpful. However, CAD systems for diagnosing ear diseases require an automatic tympanic membrane (TM) segmentation model to assist in diagnosis. This is because it is difficult to detect the TM and affected areas in an endoscopic image of the TM owing to irregular lighting. In this study, we propose a ResNet152 UNet++ image segmentation network. The proposed method applies the ResNet152 layer structure to the encoders in the UNet++ model to detect the location of the TM and affected area with high accuracy. Furthermore, the TM and affected regions can be segmented better than when using the previously proposed UNet and UNet++ models. To the best of our knowledge, this study is the first to use a UNet++-based segmentation model to segment TM areas in endoscopic images of the TM and evaluate its performance. The experiments revealed that ResNet152 UNet++ outperforms conventional methods in terms of segmentation of the TM and affected areas.

Keywords

• Convolutional neural network
• artificial neural network
• segmentation
• otitis media
• computer-aided diagnosis