Performance evaluation of attention-deep hashing based medical image retrieval in brain MRI datasets

作者：Yuping Chen, Yuping Chen, Zhian He, Muhammad Awais Ashraf, Xinwen Chen, Yu Liu, Xiangting Ding, Binbin Tong, Yijie Chen, Yijie Chen · 发表于：Journal of Radiation Research and Applied Sciences · 年份：2024 · DOI：10.1016/j.jrras.2024.100968 · 被引用次数：11 · 研究领域：Advanced Image and Video Retrieval Techniques、Image Retrieval and Classification Techniques、Advanced Neural Network Applications

Background: Brain MRI images pose significant challenges due to their complexity and voluminous data, which often hinder the accuracy of traditional image retrieval methods. In response, this research delves into a novel medical image retrieval approach grounded in attention mechanisms and deep hashing techniques. Methodology: The study enhances the network's edge perception capability by incorporating a dual mixed attention module (CBAM-MA dual attention) into the convolutional neural network architecture. This addition optimally captures edge saliency and distributional features by introducing an extra attention layer atop CBAM. Furthermore, a novel triple loss function is introduced, amalgamating Euclidean and cosine distances. This fusion exploits the strengths of both distance metrics to comprehensively consider sample geometric attributes, thereby generating more robust and expressive feature representations crucial for preserving categorical details. Results: Experimental evaluations showcase significant performance advancements in MRI image retrieval tasks using the proposed method. The average hit rate stands at 0.7783, average precision at 0.752246, and average inverse rank at 0.956721. These metrics collectively underscore the method's efficacy in enhancing the quality and expediting the diagnosis of brain diseases. Conclusion: The research underscores the critical role of attention and deep hashing techniques in augmenting the accuracy and efficiency of medical im...