Detection and Classification of Dog Skin Disease using Deep Learning

Detection and Classification of Dog Skin Disease using Deep Learning

Naresh Thoutam¹, Ishika Mandloi², Anuja Kumari³, Samruddhi Sonule⁴, Vrukshali Torawane⁵

¹Assistant Professor, Department of Computer Engineering, Sandip Institute of Technology and Research Center, Nashik, India.

²Student, Department of Computer Engineering, Sandip Institute of Technology and Research Center, Nashik, India.

³Student, Department of Computer Engineering, Sandip Institute of Technology and Research Center, Nashik, India.

⁴Student, Department of Computer Engineering, Sandip Institute of Technology and Research Center, Nashik, India.

⁵Student, Department of Computer Engineering, Sandip Institute of Technology and Research Center, Nashik, India.

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Abstract - Dogs are beloved pets and loyal companions to millions of people worldwide. Unfortunately, dogs can also suffer from a variety of skin diseases that can cause discomfort, pain, and even life-threatening complications. Some dog skin diseases can be transmitted to humans through direct contact. Early detection and treatment of these skin diseases are crucial for the health and well-being of dogs and humans. Our project aims to provide a quick and precise approach to identifying various types of skin diseases in dogs. To expedite the process of identifying and diagnosing infections related to canine interaction, we plan to utilize a machine-learning model. This approach aims to reduce the time and expertise required for accurate and consistent diagnosis, which can otherwise be challenging and time-consuming. Two models, InceptionV3 and MobileNetV2, were utilized and compared in our implementation. In the case of InceptionV3, a training accuracy of 0.99 and a validation accuracy of 0.98 were achieved. For MobileNetV2, we attained a validation accuracy of 96 and a categorical accuracy of 97.

Key Words: Dermatophytosis, zoonosis, image classification, deep learning, Transfer learning, InceptionV3, MobileNetV2, CNN, DNN.