Effective Data Optimization and Evaluation Based on Social Communication with AI-Assisted in Opportunistic Social Networks

Li, Limiao; Gou, Fangfang; Long, Huiyun; He, Ke; Wu, Jia

doi:10.1155/2022/4879557

Cited by 16 publications

(9 citation statements)

References 56 publications

(51 reference statements)

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“…Accuracy (Acc) is used to evaluate the proportion of the model's right prediction and it is denoted as [ 52 ]:

…”

Section: Methodsmentioning

confidence: 99%

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BA-GCA Net: Boundary-Aware Grid Contextual Attention Net in Osteosarcoma MRI Image Segmentation

Liu

Gou

et al. 2022

Computational Intelligence and Neuroscience

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Osteosarcoma is one of the most common bone tumors that occurs in adolescents. Doctors often use magnetic resonance imaging (MRI) through biosensors to diagnose and predict osteosarcoma. However, a number of osteosarcoma MRI images have the problem of the tumor shape boundary being vague, complex, or irregular, which causes doctors to encounter difficulties in diagnosis and also makes some deep learning methods lose segmentation details as well as fail to locate the region of the osteosarcoma. In this article, we propose a novel boundary-aware grid contextual attention net (BA-GCA Net) to solve the problem of insufficient accuracy in osteosarcoma MRI image segmentation. First, a novel grid contextual attention (GCA) is designed to better capture the texture details of the tumor area. Then the statistical texture learning block (STLB) and the spatial transformer block (STB) are integrated into the network to improve its ability to extract statistical texture features and locate tumor areas. Over 80,000 MRI images of osteosarcoma from the Second Xiangya Hospital are adopted as a dataset for training, testing, and ablation studies. Results show that our proposed method achieves higher segmentation accuracy than existing methods with only a slight increase in the number of parameters and computational complexity.

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“…Accuracy (Acc) is used to evaluate the proportion of the model's right prediction and it is denoted as [ 52 ]:

…”

Section: Methodsmentioning

confidence: 99%

“…The Dilated ResNet-D-22(DRN-D-22) [ 52 ] is chosen as the backbone and BA-GCA Net is designed based on it. Note that osteosarcoma MRI images often have large individual differences, and the relationship between pixels in one image plays a more crucial role than that among images.…”

Section: Methodsmentioning

confidence: 99%

BA-GCA Net: Boundary-Aware Grid Contextual Attention Net in Osteosarcoma MRI Image Segmentation

Liu

Gou

et al. 2022

Computational Intelligence and Neuroscience

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“…In the diagnosis of osteosarcoma, using computer-aided diagnosis technology and an artificial intelligence system as auxiliary is particularly important. Current methods for segmenting tumor regions from images can be broadly divided into three categories [ 28 ].…”

Section: Related Workmentioning

confidence: 99%

Auxiliary Segmentation Method of Osteosarcoma in MRI Images Based on Denoising and Local Enhancement

et al. 2022

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Osteosarcoma is a bone tumor which is malignant. There are many difficulties when doctors manually identify patients’ MRI images to complete the diagnosis. The osteosarcoma in MRI images is very complex, making its recognition and segmentation resource-consuming. Automatic osteosarcoma area segmentation can solve these problems to a certain extent. However, existing studies usually fail to balance segmentation accuracy and efficiency. They are either sensitive to noise with low accuracy or time-consuming. So we propose an auxiliary segmentation method based on denoising and local enhancement. The method first optimizes the osteosarcoma images, including removing noise using the Edge Enhancement based Transformer for Medical Image Denoising (Eformer) and using a non-parameter method to localize and enhance the tumor region in MRI images. Osteosarcoma was then segmented by Deep Feature Aggregation for Real-Time Semantic Segmentation (DFANet). Our method achieves impressive segmentation accuracy. Moreover, it is efficient in both time and space. It can provide information about the location and extent of the osteosarcoma as a basis for further diagnosis.

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“…Meanwhile, osteosarcoma has a wide range of local tissue development and shape [39,40]. Tese properties cause indistinct tumor boundaries and complex form structures, making it difcult to maintain edge features [41][42][43]. As a result, it is worth looking into how to segment osteosarcoma efectively and properly.…”

Section: Introductionmentioning

confidence: 99%

Auxiliary Segmentation Method of Osteosarcoma MRI Image Based on Transformer and U-Net

Liu

Zhu

et al. 2022

Computational Intelligence and Neuroscience

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One of the most prevalent malignant bone tumors is osteosarcoma. The diagnosis and treatment cycle are long and the prognosis is poor. It takes a lot of time to manually identify osteosarcoma from osteosarcoma magnetic resonance imaging (MRI). Medical image processing technology has greatly alleviated the problems faced by medical diagnoses. However, MRI images of osteosarcoma are characterized by high noise and blurred edges. The complex features increase the difficulty of lesion area identification. Therefore, this study proposes an osteosarcoma MRI image segmentation method (OSTransnet) based on Transformer and U-net. This technique primarily addresses the issues of fuzzy tumor edge segmentation and overfitting brought on by data noise. First, we optimize the dataset by changing the precise spatial distribution of noise and the data-increment image rotation process. The tumor is then segmented based on the model of U-Net and Transformer with edge improvement. It compensates for the limitations of U-semantic Net by using channel-based transformers. Finally, we also add an edge enhancement module (BAB) and a combined loss function to improve the performance of edge segmentation. The method’s accuracy and stability are demonstrated by the detection and training results based on more than 4,000 MRI images of osteosarcoma, which also demonstrate how well the method works as an adjunct to clinical diagnosis and treatment.

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Effective Data Optimization and Evaluation Based on Social Communication with AI-Assisted in Opportunistic Social Networks

Cited by 16 publications

References 56 publications

BA-GCA Net: Boundary-Aware Grid Contextual Attention Net in Osteosarcoma MRI Image Segmentation

BA-GCA Net: Boundary-Aware Grid Contextual Attention Net in Osteosarcoma MRI Image Segmentation

Auxiliary Segmentation Method of Osteosarcoma in MRI Images Based on Denoising and Local Enhancement

Auxiliary Segmentation Method of Osteosarcoma MRI Image Based on Transformer and U-Net

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