VGGNet and Attention Mechanism-Based Image Quality Assessment Algorithm in Symmetry Edge Intelligence Systems Article Swipe
Fanfan Shen
,
Haipeng Liu
,
Chao Xu
,
Lei Ouyang
,
Jun Zhang
,
Yong Chen
,
Yanxiang He
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/sym17030331
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/sym17030331
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/sym17030331
- https://www.mdpi.com/2073-8994/17/3/331/pdf?version=1740207617
- OA Status
- gold
- Cited By
- 1
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407874664
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- OpenAlex ID
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https://openalex.org/W4407874664Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/sym17030331Digital Object Identifier
- Title
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VGGNet and Attention Mechanism-Based Image Quality Assessment Algorithm in Symmetry Edge Intelligence SystemsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-02-22Full publication date if available
- Authors
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Fanfan Shen, Haipeng Liu, Chao Xu, Lei Ouyang, Jun Zhang, Yong Chen, Yanxiang HeList of authors in order
- Landing page
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https://doi.org/10.3390/sym17030331Publisher landing page
- PDF URL
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https://www.mdpi.com/2073-8994/17/3/331/pdf?version=1740207617Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2073-8994/17/3/331/pdf?version=1740207617Direct OA link when available
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Computer science, Enhanced Data Rates for GSM Evolution, Symmetry (geometry), Image (mathematics), Mechanism (biology), Image quality, Algorithm, Artificial intelligence, Quality (philosophy), Mathematics, Physics, Geometry, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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