VGGNet and Attention Mechanism-Based Image Quality Assessment Algorithm in Symmetry Edge Intelligence Systems

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 sym...

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Bibliographic Details
Published in:Symmetry (Basel) Vol. 17; no. 3; p. 331
Main Authors: Shen, Fanfan, Liu, Haipeng, Xu, Chao, Ouyang, Lei, Zhang, Jun, Chen, Yong, He, Yanxiang
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.03.2025
Subjects:
Accuracy
Algorithms
Brain
Brain cancer
Brain research
Brain tumors
Datasets
Decision making
Disease
Image quality
Intelligence
Internet of Things
Magnetic resonance imaging
Medical diagnosis
Medical imaging
Medical imaging equipment
Methods
Modules
Neural networks
Performance enhancement
Quality assessment
Quality management
Salience
Streaming media
Symmetry
Tissues
Tumors
Visual perception
Visual perception driven algorithms
Workloads
ISSN:2073-8994, 2073-8994
Online Access:Get full text
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Summary: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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ISSN:2073-8994
2073-8994
DOI:10.3390/sym17030331