Utilizing Artificial Intelligence for Early Detection of Common Canine Diseases

Thesis Abstract

Abstract
The advancement of Artificial Intelligence (AI) technologies has revolutionized various industries, including healthcare. This thesis explores the application of AI in veterinary medicine, specifically focusing on the early detection of common canine diseases. The objective of this study is to develop a system that utilizes AI algorithms to analyze diagnostic data and improve the accuracy and efficiency of disease detection in dogs. Chapter 1 provides an introduction to the research topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. Chapter 2 comprises a comprehensive literature review that examines existing research on AI in veterinary medicine, canine diseases, diagnostic techniques, and AI algorithms relevant to disease detection. In Chapter 3, the research methodology is outlined, detailing the data collection process, AI model development, training and validation procedures, and the evaluation metrics used to assess the performance of the system. Various aspects of the methodology, such as data preprocessing techniques, feature selection methods, and model optimization strategies, are discussed in detail. Chapter 4 presents an in-depth discussion of the findings obtained from the experimental evaluation of the AI system. The results of the study are analyzed, and the performance of the system in detecting common canine diseases is evaluated based on key metrics such as accuracy, sensitivity, specificity, and predictive values. The limitations of the study and potential areas for future research are also discussed. Finally, Chapter 5 concludes the thesis by summarizing the key findings, highlighting the contributions of the study to the field of veterinary medicine, and discussing the implications of the research findings for clinical practice. The study demonstrates the potential of AI technologies to enhance disease detection in dogs, leading to early diagnosis, timely intervention, and improved outcomes for canine patients. In conclusion, this thesis contributes to the growing body of research on the application of AI in veterinary medicine and provides valuable insights into the development of AI-based systems for early detection of common canine diseases. The findings of this study have the potential to drive innovation in veterinary healthcare and improve the quality of care for companion animals.