A Deep Learning Approach to Computer-Aided Screening and Early Diagnosis of Middle Ear Disease

This article introduces a deep learning approach to computer-aided screening and early diagnosis of middle ear diseases such as earwax, otitis externa, tympanosclerosis, and ear ventilation tubes. The timely detection of middle ear conditions is crucial for effective treatment and prevention of complications. The proposed system utilizes a deep neural network trained on a large dataset of middle ear images obtained through advanced diagnostic imaging techniques. The system automatically analyzes these images by leveraging deep learning to provide accurate and efficient screening and diagnostic support. The proposed system aims to assist healthcare professionals in accurate and efficient early diagnosis and screening of critical conditions, leading to improved patient outcomes and optimized treatment plans. The proposed model presents a deep learning 2D-CNN model for binary and multi-class classification of ear diseases in medical healthcare. The results demonstrate its effectiveness and superiority compared with the traditional machine learning approaches.