Comparative Analysis of Binarization Methods For Medical Image Hashing On Odir Dataset
This work addresses medical image retrieval and device inventory management by offering a practical balance of accuracy and efficiency, though it is incremental as it evaluates existing methods on a specific dataset.
The study compared four binarization methods for medical image hashing on the ODIR dataset, finding that Supervised Discrete Hashing (SDH) achieved the best performance with an mAP@100 of 0.9184 using only 32-bit codes.
In this study, we evaluated four binarization methods. Locality-Sensitive Hashing (LSH), Iterative Quantization (ITQ), Kernel-based Supervised Hashing (KSH), and Supervised Discrete Hashing (SDH) on the ODIR dataset using deep feature embeddings. Experimental results show that SDH achieved the best performance, with an mAP@100 of 0.9184 using only 32-bit codes, outperforming LSH, ITQ, and KSH. Compared with prior studies, our method proved highly competitive: Fang et al. reported 0.7528 (Fundus-iSee, 48 bits) and 0.8856 (ASOCT-Cataract, 48 bits), while Wijesinghe et al. achieved 94.01 (KVASIR, 256 bits). Despite using significantly fewer bits, our SDH-based framework reached retrieval accuracy close to the state-of-the-art. These findings demonstrate that SDH is the most effective approach among those tested, offering a practical balance of accuracy, storage, and efficiency for medical image retrieval and device inventory management.