A Comparative Study of Image Retrieval Algorithm in Medical Imaging

Abstract

In recent times, digital environments have become more complex, and the need for secure, efficient, and reliable identification systems is growing in demand. Consequently, image retrieval has emerged as a critical area focusing on artificial intelligence and machine learning applications. Medical image retrieval has become increasingly crucial in today's healthcare field, as it involves accurate diagnostics, treatment planning, and advanced medical research. As the quantity of medical imaging data grows rapidly, the ability to efficiently and accurately retrieve relevant images from extensive datasets becomes critical. Advanced retrieval systems, such as content-based image retrieval, are imperative for managing complex data, ensuring that healthcare professionals can access the most relevant information to improve patient outcomes and advance medical knowledge. This paper compares three algorithms: Scale Invariant Feature Transform, Speeded Robust Features, and Convolutional Neural Networks in the context of two medical image datasets, ImageCLEF and Unifesp. The findings highlight the trade-offs between precision and recall for each algorithm, providing invaluable insights into selecting the most suitable algorithm for specific tasks. The study evaluates the algorithms based on precision and recall, two critical performance metrics in image retrieval.