CANINE INJURY DETECTION: A COMPARATIVE ANALYSIS OF IMAGE CLASSIFICATION MODELS

The swift and accurate detection of canine injuries is crucial for timely medical intervention and enhancing public safety. This study presents a comprehensive analysis of three image classification models—Convolutional Neural Networks (CNN), Random Forest, and Support Vector Machines (SVM)— for detecting injuries in dogs. Utilizing a meticulously curated dataset of over 2000 images, the research emphasizes robust data gathering, cleaning, and preprocessing techniques. Experimental results reveal that CNN outperforms with an accuracy of 75.78%, followed closely by Random Forest and SVM. The findings highlight the strengths and limitations of each model, offering valuable insights into their applicability in real-world scenarios. This research underscores the potential of advanced machine learning models in promoting animal welfare and lays a foundation for further advancements in the field of computer vision applications in veterinary care.