Comprehensive comparison of modified deep convolutional neural networks for automated detection of external and middle ear conditions

Abstract

Otitis media disease, a frequent childhood ailment, could have severe repercussions, including mortality. This disease induces permanent hearing loss, commonly seen in developing countries with limited medical resources. It is estimated that approximately 21,000 people worldwide die from reasons related to this disease each year. The main aim of this study is to develop a model capable of detecting external and middle ear conditions. Experiments were conducted to find the most successful model among the modified deep convolutional neural networks within two scenarios. According to the results, the modified EfficientNetB7 model could detect normal, chronic otitis media, earwax, myringosclerosis cases with high accuracy in Scenario 2. This model offers average values of 99.94% accuracy, 99.86% sensitivity, 99.95% specificity, and 99.86% precision. An expert system based on this model is expected to provide a second opinion to doctors in detecting external and middle ear conditions, particularly in primary healthcare institutions and hospitals lacking field specialists.