| 要約: | The proliferation of medical imaging in clinical diagnostics has led to an overwhelming volume of image data, presenting a challenge for efficient storage, management, and retrieval. Specifically, the rapid growth in the use of imaging modalities such as Computed Tomography (CT) and X-rays has outpaced the capabilities of conventional retrieval systems, necessitating more sophisticated approaches to assist in clinical decision-making and research. Our study introduces a novel deep hash coding-based Content-Based Medical Image Retrieval (CBMIR) framework that uses a convolutional neural network (CNN) combined with hash coding for efficient and accurate retrieval. The model integrates a Dense block-based feature learning network, a hash learning block, and a spatial attention block to enhance feature extraction specific to medical imaging. We reduce dimensionality by applying the Reconstruction Independent Component Analysis (RICA) algorithm while preserving diagnostic information. The framework achieves a mean average precision (mAP) of 0.85 on ChestX-ray8, 0.82 on TCIA-CT, 0.84 on MIMIC-CXR, and 0.82 on LIDC-IDRI datasets, with retrieval times of 675 ms, 663 ms, 735 ms, and 748 ms, respectively. Comparisons with ResNet and DenseNet confirm the effectiveness of our model, enhancing medical image retrieval significantly for clinical decision-making and research.
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