A management method of chronic diseases in the elderly based on IoT security environment

Qin, Yue; Li, Xiaoli; Wu, Jia; Yu, Keping

doi:10.1016/j.compeleceng.2022.108188

Cited by 20 publications

(13 citation statements)

References 18 publications

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“…Image processing technology is gradually becoming more frequently employed in disease diagnosis, treatment, and prognosis to aid clinicians in clinical diagnosis and increase disease diagnosis efficiency [ 61 ]. In addition, due to the complexity of osteosarcoma MIR images and the diversity of tumors, existing detection methods do not achieve ideal segmentation results [ 62 ]. This study offers a segmentation approach (OSTransnet) for osteosarcoma MRI images with edge enhancement features based on Transformer and U-Net, which is primarily intended to assist clinicians in more precisely and rapidly diagnosing osteosarcoma lesions areas by recognizing osteosarcoma MRI pictures.…”

Section: System Model Designmentioning

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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Section: System Model Designmentioning

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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“…[ 46 , 47 , 48 , 49 ]. Due to the complexity of histopathological images of osteosarcoma, it is difficult for non-professionals to distinguish them [ 50 , 51 ]. The development of an automated and accurate histopathology image analysis system is of great importance to assist pathologists and clinical specialists in their work.…”

Section: System Designmentioning

confidence: 99%

A Multimodal Auxiliary Classification System for Osteosarcoma Histopathological Images Based on Deep Active Learning

Gou

Zhu

et al. 2022

Healthcare

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Histopathological examination is an important criterion in the clinical diagnosis of osteosarcoma. With the improvement of hardware technology and computing power, pathological image analysis systems based on artificial intelligence have been widely used. However, classifying numerous intricate pathology images by hand is a tiresome task for pathologists. The lack of labeling data makes the system costly and difficult to build. This study constructs a classification assistance system (OHIcsA) based on active learning (AL) and a generative adversarial network (GAN). The system initially uses a small, labeled training set to train the classifier. Then, the most informative samples from the unlabeled images are selected for expert annotation. To retrain the network, the final chosen images are added to the initial labeled dataset. Experiments on real datasets show that our proposed method achieves high classification performance with an AUC value of 0.995 and an accuracy value of 0.989 using a small amount of labeled data. It reduces the cost of building a medical system. Clinical diagnosis can be aided by the system’s findings, which can also increase the effectiveness and verifiable accuracy of doctors.

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“…It is the basis for further quantitative analysis or tissue classification. Segmentation is usually achieved by drawing a region of interest (ROI) within the tumor margin [ 19 , 20 , 21 ]. Compared to manual segmentation, automated methods are generally faster, more objective, and provide more accurate results [ 22 , 23 ].…”

Section: Introductionmentioning

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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A management method of chronic diseases in the elderly based on IoT security environment

Cited by 20 publications

References 18 publications

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

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

A Multimodal Auxiliary Classification System for Osteosarcoma Histopathological Images Based on Deep Active Learning

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

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