RAFIKI: Retrieval-Based Application for Imaging and Knowledge Investigation

Medical exams, such as CT scans and mammograms, are obtained and stored every day in hospitals all over the world, including images, patient data, and medical reports. It is paramount to have tools and systems to improve computer-aided diagnoses based on such huge volumes of stored information. The Content-Based Image Retrieval (CBIR) is a powerful paradigm to help reaching such a goal, providing physicians with intelligent retrieval tools to present him/her with similar or complementary cases, in which visual characteristics improve textual data. Employing comparative inspection on previous cases, the physician can obtain a more comprehensive understanding of the case he/she is working on. Current hospital systems do not carry native CBIR functionalities yet, relying on add-on subsystems, which often do not adhere to the existing relational database infrastructures. In this work, we propose RAFIKI, a software prototype that extends the Relational Database Management System (RDBMS) PostgreSQL, providing native support for CBIR functionalities, modular extensibility, and seamless integration for data science tools, such as Python and R. We show the applicability of our system by evaluating three clinical scenarios, performing queries over a real-world image dataset of lung exams. Our results spot actual potential in promoting informed decision-making from the physician’s perspective. Besides, the system exhibited a higher performance when compared to previous systems found in the literature. Moreover, RAFIKI contributes with a model to establish how to put together CBIR concepts and relational data, providing a powerful design for further development of theoretical and practical concepts and tools.