Design and Implementation of AI-Based Early Detection of Zoonotic Diseases from Animal Skin

Abstract

Zoonotic diseases, which are transmitted from animals to humans, pose a significant threat to global public health and livestock productivity. Early detection of these diseases is crucial to prevent large-scale outbreaks and economic losses. This research focuses on the design and implementation of an artificial intelligence (AI)-based system for the early detection of zoonotic diseases using animal skin images. The proposed system employs advanced image processing and deep learning techniques to automatically identify visual symptoms such as lesions, rashes, or discolorations that indicate possible infections. Convolutional Neural Networks (CNNs) are utilized for feature extraction and classification of various skin conditions, distinguishing between healthy and infected animals with high accuracy. A user-friendly web or mobile interface is developed to enable farmers and veterinarians to upload images for instant diagnosis and receive recommendations for prompt intervention. Experimental results demonstrate that the system can accurately detect early signs of zoonotic diseases, thereby supporting proactive veterinary care and contributing to the prevention of disease transmission from animals to humans.