VGGNet and Attention Mechanism-Based Image Quality Assessment Algorithm in Symmetry Edge Intelligence Systems
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| Опубліковано в:: | Symmetry vol. 17, no. 3 (2025), p. 331 |
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| Автор: | |
| Інші автори: | , , , , , |
| Опубліковано: |
MDPI AG
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| Предмети: | |
| Онлайн доступ: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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MARC
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| 001 | 3181699617 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2073-8994 | ||
| 024 | 7 | |a 10.3390/sym17030331 |2 doi | |
| 035 | |a 3181699617 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231635 |2 nlm | ||
| 100 | 1 | |a Shen, Fanfan |u School of Computer Science, Nanjing Audit University, Nanjing 211815, China; <email>ffshen@nau.edu.cn</email> (F.S.); | |
| 245 | 1 | |a VGGNet and Attention Mechanism-Based Image Quality Assessment Algorithm in Symmetry Edge Intelligence Systems | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Accuracy | ||
| 653 | |a Datasets | ||
| 653 | |a Internet of Things | ||
| 653 | |a Visual perception | ||
| 653 | |a Brain cancer | ||
| 653 | |a Salience | ||
| 653 | |a Disease | ||
| 653 | |a Brain research | ||
| 653 | |a Medical imaging | ||
| 653 | |a Symmetry | ||
| 653 | |a Brain | ||
| 653 | |a Modules | ||
| 653 | |a Workloads | ||
| 653 | |a Visual perception driven algorithms | ||
| 653 | |a Medical diagnosis | ||
| 653 | |a Quality assessment | ||
| 653 | |a Performance enhancement | ||
| 653 | |a Tumors | ||
| 653 | |a Neural networks | ||
| 653 | |a Magnetic resonance imaging | ||
| 653 | |a Decision making | ||
| 653 | |a Intelligence | ||
| 653 | |a Methods | ||
| 653 | |a Algorithms | ||
| 653 | |a Image quality | ||
| 653 | |a Streaming media | ||
| 653 | |a Tissues | ||
| 700 | 1 | |a Liu, Haipeng |u School of Computer Science, Nanjing Audit University, Nanjing 211815, China; <email>ffshen@nau.edu.cn</email> (F.S.); | |
| 700 | 1 | |a Xu, Chao |u School of Computer Science, Nanjing Audit University, Nanjing 211815, China; <email>ffshen@nau.edu.cn</email> (F.S.); | |
| 700 | 1 | |a Ouyang, Lei |u North Information Control Research Academy Group Company Limited, Nanjing 221000, China | |
| 700 | 1 | |a Zhang, Jun |u College of Software, East China University of Science and Technology, Nanchang 330013, China | |
| 700 | 1 | |a Chen, Yong |u School of Computer Science, Nanjing Audit University, Nanjing 211815, China; <email>ffshen@nau.edu.cn</email> (F.S.); | |
| 700 | 1 | |a He, Yanxiang |u School of Computer Science, Wuhan University, Wuhan 430072, China | |
| 773 | 0 | |t Symmetry |g vol. 17, no. 3 (2025), p. 331 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3181699617/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3181699617/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3181699617/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |