A user cache management and cooperative transmission mechanism based on edge community computing in opportunistic social networks

Xiong, Wangping; Chen, Hao; Jin, Ying; Yang, Ming; Zhou, Xian; Wu, Jia

doi:10.1049/cmu2.12458

Cited by 6 publications

(4 citation statements)

References 41 publications

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“…With the development of computer image processing technology, artificial intelligence solutions are widely used in the medical field. Its importance is to solve the problems of high consumption of medical resources and low efficiency of disease diagnosis in developing countries [ 43 , 44 , 45 , 46 ]. However, such methods still present major challenges in the recognition of tumors.…”

Section: Methodsmentioning

confidence: 99%

Artificial Intelligence-Aided Diagnosis Solution by Enhancing the Edge Features of Medical Images

Liu

et al. 2023

Diagnostics

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Bone malignant tumors are metastatic and aggressive. The manual screening of medical images is time-consuming and laborious, and computer technology is now being introduced to aid in diagnosis. Due to a large amount of noise and blurred lesion edges in osteosarcoma MRI images, high-precision segmentation methods require large computational resources and are difficult to use in developing countries with limited conditions. Therefore, this study proposes an artificial intelligence-aided diagnosis scheme by enhancing image edge features. First, a threshold screening filter (TSF) was used to pre-screen the MRI images to filter redundant data. Then, a fast NLM algorithm was introduced for denoising. Finally, a segmentation method with edge enhancement (TBNet) was designed to segment the pre-processed images by fusing Transformer based on the UNet network. TBNet is based on skip-free connected U-Net and includes a channel-edge cross-fusion transformer and a segmentation method with a combined loss function. This solution optimizes diagnostic efficiency and solves the segmentation problem of blurred edges, providing more help and reference for doctors to diagnose osteosarcoma. The results based on more than 4000 osteosarcoma MRI images show that our proposed method has a good segmentation effect and performance, with Dice Similarity Coefficient (DSC) reaching 0.949, and show that other evaluation indexes such as Intersection of Union (IOU) and recall are better than other methods.

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Section: Methodsmentioning

confidence: 99%

Artificial Intelligence-Aided Diagnosis Solution by Enhancing the Edge Features of Medical Images

Liu

et al. 2023

Diagnostics

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Section: Simulation Analysismentioning

confidence: 99%

“…In this paper, all algorithms are tested in the same environment to compare their performance in the same indicators. These metrics include message delivery rate, average overhead, energy consumption, and average end‐to‐end latency [58, 59]. The message delivery rate reflects the degree of connectivity and accessibility in the social network of opportunities.…”

Section: System Model Designmentioning

confidence: 99%

An effective data communication community establishment scheme in opportunistic networks

Hao

Gou

2023

IET Communications

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The network transmission speed has been greatly improved, thanks to the power of 5G technology. The millisecond‐level communication delay has made a qualitative leap in communication quality. However, the sharp increase in the number of nodes connected to the internet has resulted in an explosion of traffic. Ensuring stable network transmission in the face of large data volumes has become an urgent problem to be solved. Existing research mainly optimizes for low data volumes of nodes, and cannot dynamically adapt to node transmission load to cope with explosive growth. This is because the node's cache is always at a high level, which causes community communication to be blocked. To address this issue, the authors have designed an effective data communication community establishment scheme. By adding a data information flow (DIF) attribute to each user and using the dynamic feedback adjustment (DFA) mechanism, the authors can combine the entire communication community and control the user's data information flow concentration at a relatively low level. This degree of optimization ensures that the user node maintains a relatively stable state for fast communication. The authors design an effective data communication community establishment scheme. By adding a data information flow (DIF) attribute to each user, using the dynamic feedback adjustment mechanism and combining the entire communication community, the user's data information flow concentration is controlled at a relatively low level.

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“…In recent years, with the increasing attention to AI, some medical image detection technologies have also been applied to the diagnosis of osteosarcoma [ 29 , 30 ]. Although these techniques can to some extent detect the position and margins of the tumor, the accuracy of detection is not high due to the variability of osteosarcoma [ 31 , 32 , 33 , 34 ]. The effect of existing medical image diagnosis technology in osteosarcoma image segmentation is not as expected [ 35 ].…”

Section: Introductionmentioning

confidence: 99%

AI-Assisted Diagnosis and Decision-Making Method in Developing Countries for Osteosarcoma

Tang

Huang

et al. 2022

Healthcare

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Osteosarcoma is a malignant tumor derived from primitive osteogenic mesenchymal cells, which is extremely harmful to the human body and has a high mortality rate. Early diagnosis and treatment of this disease is necessary to improve the survival rate of patients, and MRI is an effective tool for detecting osteosarcoma. However, due to the complex structure and variable location of osteosarcoma, cancer cells are highly heterogeneous and prone to aggregation and overlap, making it easy for doctors to inaccurately predict the area of the lesion. In addition, in developing countries lacking professional medical systems, doctors need to examine mass of osteosarcoma MRI images of patients, which is time-consuming and inefficient, and may result in misjudgment and omission. For the sake of reducing labor cost and improve detection efficiency, this paper proposes an Attention Condenser-based MRI image segmentation system for osteosarcoma (OMSAS), which can help physicians quickly locate the lesion area and achieve accurate segmentation of the osteosarcoma tumor region. Using the idea of AttendSeg, we constructed an Attention Condenser-based residual structure network (ACRNet), which greatly reduces the complexity of the structure and enables smaller hardware requirements while ensuring the accuracy of image segmentation. The model was tested on more than 4000 samples from two hospitals in China. The experimental results demonstrate that our model has higher efficiency, higher accuracy and lighter structure for osteosarcoma MRI image segmentation compared to other existing models.

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A user cache management and cooperative transmission mechanism based on edge community computing in opportunistic social networks

Cited by 6 publications

References 41 publications

Artificial Intelligence-Aided Diagnosis Solution by Enhancing the Edge Features of Medical Images

Artificial Intelligence-Aided Diagnosis Solution by Enhancing the Edge Features of Medical Images

An effective data communication community establishment scheme in opportunistic networks

AI-Assisted Diagnosis and Decision-Making Method in Developing Countries for Osteosarcoma

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