Abstract

Early and accurate diagnosis of diseases in animals is essential for improving veterinary healthcare and reducing mortality. Traditional manual interpretation of X-ray radiographs is often time-consuming and prone to human error, especially in rural veterinary practices. This research presents an automated disease detection framework using Digital Image Processing (DIP) techniques applied to animal X-ray images. The proposed methodology includes image preprocessing, segmentation, feature extraction, and classification using both classical machine learning and deep learning algorithms. Various abnormalities such as bone fractures, lung infections, and soft-tissue irregularities were analyzed. Experimental results demonstrate that deep learning models, particularly Convolutional Neural Networks (CNNs), significantly outperform conventional approaches with improved accuracy and robustness. The study concludes that digital image processing provides an efficient, scalable, and cost-effective diagnostic support system for veterinary medicine.

Authors

G.G. Rajput, Sumitra M. Mudda
Karnataka State Women’s University, India

Keywords

Digital Image Processing, Animal X-Ray, Disease Detection, Image Segmentation, Feature Extraction, Classification, Machine Learning, Deep Learning, Veterinary Diagnosis