Personal Assistant Development by CED (Canine Eye-disease Detection)

Kyunghan Chun


In this paper, we develop a deep learning-based canine eye disease detection and utilize it to create a dog health management system. With the recent surge in the number of pet dogs, ensuring their well-being has become crucial. We achieve this by applying lightweight deep learning methods like MobileNet and SqueezeNet to mobile devices, enabling regular monitoring of a pet's eye health. Additionally, we provide a GPS-based search feature for nearby hospitals, facilitating swift response to diseases. The validity of the developed method is demonstrated through experiments on 5 eye diseases. The results confirm the importance of considering appropriate recognition rates and recognizability metrics, as outcomes may vary depending on the applied deep learning approach.


CED (Canine Eye-disease Detection); Deep Learning; MobileNet; SqueezeNet


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Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
ISSN 2089-3272

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