Fuzzy-AttnNet: a deep learning framework for image retrieval using fuzzy feature fusion

Image retrieval is the process of searching for similar images based on their visual content, which has significant applications in the field of medical imaging. Quickly retrieving similar images from a medical image database can assist doctors in rapid browsing and diagnosis. Although feature fusion-based descriptors have achieved great success in the field of image retrieval, there are relatively few cases of using fusion feature descriptors for image retrieval in the medical imaging field. To address this gap, we propose a novel feature extraction model, Fuzzy-AttnNet, aimed at enhancing the performance of medical image retrieval using fusion descriptors. The model simulates dilated convolutions and self-attention mechanisms, comprehensively modeling both local and global features of medical images and performing feature fusion. The fused descriptors are then applied to image retrieval. Additionally, Fuzzy-AttnNet employs a parallel optimization strategy using Arcface loss and Softmax loss, which increases intra-class spacing and significantly improves the model's recognition performance across different categories. In subclass classification tasks, Fuzzy-AttnNet demonstrates significant advantages, with an average precision improvement of approximately 10% and a recall increase of about 30%. The experimental results indicate that our proposed feature extraction model significantly enhances the accuracy of medical image retrieval.