文献类型：期刊文献

英文题名：VGGNet and Attention Mechanism-Based Image Quality Assessment Algorithm in Symmetry Edge Intelligence Systems

作者：Shen, Fanfan[1];Liu, Haipeng[1];Xu, Chao[1];Ouyang, Lei[2];Zhang, Jun[3];Chen, Yong[1];He, Yanxiang[4]

机构：[1]Nanjing Audit Univ, Sch Comp Sci, Nanjing 211815, Peoples R China;[2]North Informat Control Res Acad Grp Co Ltd, Nanjing 221000, Peoples R China;[3]East China Univ Sci & Technol, Coll Software, Nanchang 330013, Peoples R China;[4]Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China

年份：2025

卷号：17

期号：3

外文期刊名：SYMMETRY-BASEL

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001453843700001)】；

基金：This work was supported by the Basic Science (Natural Science) Research Project of Colleges and Universities in Jiangsu Province (24KJA520005, 22KJA520004), and the National Natural Science Foundation of China (61902189, 62472227, 62162002 and 61972293).

语种：英文

外文关键词：image quality assessment; attention mechanism; visual perception characteristics; medical images; symmetry edge systems

摘要：With the rapid development of Internet of Things (IoT) technology, the number of devices connected to the network is exploding. How to improve the performance of edge devices has become an important challenge. Research on quality evaluation algorithms for brain tumor images remains scarce within symmetry edge intelligence systems. Additionally, the data volume in brain tumor datasets is frequently inadequate to support the training of neural network models. Most existing non-reference image quality assessment methods are based on natural statistical laws or construct a single-network model without considering visual perception characteristics, resulting in significant differences between the final evaluation results and subjective perception. To address these issues, we propose the AM-VGG-IQA (Attention Module Visual Geometry Group Image Quality Assessment) algorithm and extend the brain tumor MRI dataset. Visual saliency features with attention mechanism modules are integrated into AM-VGG-IQA. The integration of visual saliency features brings the evaluation outcomes of the model more in line with human perception. Meanwhile, the attention mechanism module cuts down on network parameters and expedites the training speed. For the brain tumor MRI dataset, our model achieves 85% accuracy, enabling it to effectively accomplish the task of evaluating brain tumor images in edge intelligence systems. Additionally, we carry out cross-dataset experiments. It is worth noting that, under varying training and testing ratios, the performance of AM-VGG-IQA remains relatively stable, which effectively demonstrates its remarkable robustness for edge applications.