The Design of Convolutional Neural Networks Model for Classification of Ear Diseases on Android Mobile Devices

I Gede Suta Wijaya - University of Mataram, Mataram, Indonesia
Heru Mulyana - Universiti Teknologi Mara, Selangor, Malaysia
Hamsu Kadriyan - University of Mataram, Mataram, Indonesia
Riska Fa'rifah - Universitas Telkom, Bandung, Indonesia

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An otorhinolaryngologist (ORL) or general practitioner diagnoses ear disease based on ear image information. However, general practitioners refer patients to ORL for chronic ear disease because the image of ear disease has high complexity, variety, and little difference between diseases. An artificial intelligence-based approach is needed to make it easier for doctors to diagnose ear diseases based on ear image information, such as the Convolutional Neural Network (CNN). This paper describes how CNN was designed to generate CNN models used to classify ear diseases. The model was developed using an ear image dataset from the practice of an ORL at the University of Mataram Teaching Hospital. This work aims to find the best CNN model for classifying ear diseases applicable to android mobile devices. Furthermore, the best CNN model is deployed for an Android-based application integrated with the Endoscope Ear Cleaning Tool Kit for registering patient ear images. The experimental results show 83% accuracy, 86% precision, 86% recall, and 4ms inference time. The application produces a System Usability Scale of 76.88% for testing, which shows it is easy to use. This achievement shows that the model can be developed and integrated into an ENT expert system. In the future, the ENT expert system can be operated by workers in community health centres/clinics to assist leading health them in diagnosing ENT diseases early.


Artificial intelligence; Convolutional Neural Network; ear disease; image classification; Android

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