  Nearest neighbor search (NNS) aims to locate the points in high-dimensional
space that is closest to the query point. The brute-force approach for finding
the nearest neighbor becomes computationally infeasible when the number of
points is large. The NNS has multiple applications in medicine, such as
searching large medical imaging databases, disease classification, diagnosis,
etc. With a focus on medical imaging, this paper proposes DenseLinkSearch an
effective and efficient algorithm that searches and retrieves the relevant
images from heterogeneous sources of medical images. Towards this, given a
medical database, the proposed algorithm builds the index that consists of
pre-computed links of each point in the database. The search algorithm utilizes
the index to efficiently traverse the database in search of the nearest
neighbor. We extensively tested the proposed NNS approach and compared the
performance with state-of-the-art NNS approaches on benchmark datasets and our
created medical image datasets. The proposed approach outperformed the existing
approach in terms of retrieving accurate neighbors and retrieval speed. We also
explore the role of medical image feature representation in content-based
medical image retrieval tasks. We propose a Transformer-based feature
representation technique that outperformed the existing pre-trained Transformer
approach on CLEF 2011 medical image retrieval task. The source code of our
experiments are available at https://github.com/deepaknlp/DLS.
