Ear Disease Detection Using Deep Learning

Abstract

Early diagnosis of ear diseases is essential to prevent serious complications such as chronic infections and permanent hearing loss. Conventional otoscopic diagnosis relies heavily on experienced clinicians and specialized equipment, which limits healthcare accessibility in rural and underserved regions. This paper presents an automated ear disease detection system based on Convolutional Neural Networks (CNNs) for binary classification of otoscopic images into normal and abnormal categories. The proposed framework incorporates image preprocessing and data augmentation techniques to improve image quality, increase dataset diversity, and enhance the robustness of the classification model. The model was trained and evaluated using a publicly available dataset containing 1,370 otoscopic images. Experimental results demonstrate an overall classification accuracy of 91.2%, with a precision of 90.4%, a recall of 89.8%, and an F1-score of 90.1%. The proposed system effectively distinguishes normal ear conditions from abnormalities, including Acute Otitis Media (AOM) and other infectious ear diseases. Furthermore, the trained CNN model was integrated into a Flask-based web application to provide an accessible, real-time diagnostic support tool for primary healthcare settings. The proposed solution has the potential to assist clinicians in early screening, improve diagnostic efficiency, and enhance healthcare accessibility in resource-limited environments.