Advancements in Non-Linear Content-Based Image Retrieval (CBIR) Systems for Image Analysis

Abstract

Content-Based Image Retrieval (CBIR) systems have witnessed significant advancements in recent years, particularly in the domain of medical image analysis. This paper provides an introductory overview of these advancements in CBIR systems tailored for medical image analysis. The evolution of CBIR techniques and their applications in the medical field are discussed, highlighting their importance in tasks such as diagnosis, treatment planning, and research. Various modalities of medical imaging, including X-rays, CT scans, MRI, and PET, are addressed in the context of CBIR systems. Lung cancer incidence rates vary across regions, with the highest rates observed in North America, Europe, Eastern Asia, and South America, and the lowest rates in certain geographic areas. Moreover, the integration of machine learning and deep learning approaches in CBIR systems is explored, emphasizing their role in enhancing image retrieval accuracy and efficiency. Additionally, challenges and future directions in the development and deployment of CBIR systems for medical image analysis are discussed, providing insights into potential avenues for further research and innovation in this rapidly evolving field.