Abstract: Progressing advances in medical imaging industries to conceptualize a comprehensive automated system of medical procedures, diagnosis, treatment, and prediction. The success of this system depends heavily on the robustness, accuracy, and speed of retrieval systems. This is why growing requirements for content-based image retrieval (CBIR) systems apply to medical images. CBIR also was known as query by image content (QBIC) and content-based visual information retrieval (CBVIR) is the application of computer vision techniques to the image retrieval problem. CBIR system is valuable in medical systems as it provides retrieval of the images from the large annotated image dataset. When a clinician is looking at a new image to interpret, the images from the dataset that are most similar are recovered via CBIR system and presented together with their annotation content, thus showing the specific pathophysiological features and diagnosis of similar images. In addition, it can be used to retrieve similar cases and hence make a classification of the given images’ modalities (CT, MRI, x-ray image). This classification can be helpful in helping physicians make the appropriate decisions. Thus, improving CBIR systems can benefit not only the management of increasingly large image collections, but also to aid clinical care, biomedical research, and education.