Smartphone-Based Automated Diagnosis of Otitis Media: A Neural Network Approach
Özet
Otitis media is a prevalent childhood illness, particularly in resource-constrained regions where access to ear specialists and specialist equipment is limited. This paper presents an extended system for automated diagnosis of middle ear pathology, specifically otitis media, that can be used on a smartphone with an internet connection. The system incorporates a neural network as a classifier and compares its performance to a previously proposed decision tree. It achieves high accuracy in diagnosing various middle ear conditions, including normal tympanic membrane, obstructing wax or foreign bodies, acute otitis media (AOM), otitis media with effusion (OME), and chronic suppurative otitis media (CSOM). The average classification accuracy of the proposed system is 81.58% (decision tree) and 86.84% (neural network) when using commercial video-otoscopes. The system utilizes 80% of the 389 images for training and 20% for testing and validation.
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