AI BASED ANIMAL SKIN DISORDERED DETECTION SYSTEM

Skin disorders in animals are prevalent and can significantly affect their health and quality of life. Accurate and timely diagnosis of these conditions is crucial for effective treatment and management. This paper presents an AI-based system designed to automate the detection and classification of skin disorders in animals using advanced image processing and deep learning techniques. The system leverages Convolutional Neural Networks (CNNs) to analyze images of animal skin lesions and classify them into various disorder categories. Our methodology involves collecting a comprehensive dataset of animal skin images from various sources, followed by extensive preprocessing to enhance image quality and standardize input data. We utilize transfer learning to fine-tune pre-trained CNN models, ensuring robust feature extraction and accurate classification. The system is evaluated using multiple performance metrics, including accuracy, precision, recall, and F1-score, demonstrating high reliability in diagnosing skin disorders. Real-world testing in veterinary clinics validates the practical applicability of the system, showing that it can assist veterinarians in making quicker and more accurate diagnoses. By providing an automated, objective, and efficient diagnostic tool, our system has the potential to significantly improve veterinary care and enhance animal welfare. Future work will focus on expanding the dataset, refining the model, and integrating additional diagnostic modalities to further enhance system performance.