Triad link prediction method based on the evolutionary analysis with IoT in opportunistic social networks

Computational and Mathematical Methods in Medicine

et al. 2022

Self Cite

Osteosarcoma is the most common primary malignant bone tumor in children and adolescents. It has a high degree of malignancy and a poor prognosis in developing countries. The doctor manually explained that magnetic resonance imaging (MRI) suffers from subjectivity and fatigue limitations. In addition, the structure, shape, and position of osteosarcoma are complicated, and there is a lot of noise in MRI images. Directly inputting the original data set into the automatic segmentation system will bring noise and cause the model’s segmentation accuracy to decrease. Therefore, this paper proposes an osteosarcoma MRI image segmentation system based on a deep convolution neural network, which solves the overfitting problem caused by noisy data and improves the generalization performance of the model. Firstly, we use Mean Teacher to optimize the data set. The noise data is put into the second round of training of the model to improve the robustness of the model. Then, we segment the image using a deep separable U-shaped network (SepUNet) and conditional random field (CRF). SepUnet can segment lesion regions of different sizes at multiple scales; CRF further optimizes the boundary. Finally, this article calculates the area of the tumor area, which provides a more intuitive reference for assisting doctors in diagnosis. More than 80000 MRI images of osteosarcoma from three hospitals in China were tested. The results show that the proposed method guarantees the balance of speed, accuracy, and cost under the premise of improving accuracy.

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Segmentation Medical Assistance System for MRI Images of Osteosarcoma in Developing Countries

Yang

Computational and Mathematical Methods in Medicine

et al. 2022

Self Cite

“…On the other hand, Masud et al [ 24 ] used modern deep learning CNN to build a classification and recognition framework for lung and colon cancer with an accuracy of 96.33%. Kriegsmann et al [ 25 ] and Wang et al [ 26 ] used multiple CNN models to experimentally evaluate histopathological images of NSCLC subtypes for classification. In particular, Han et al [ 27 ], together with Chaunzwa et al [ 28 ], verified that the CNN model VGG-16 outperformed other conventional machine learning algorithms in terms of the classification and recognition accuracy of NSCLC on PET/CT images.…”

Section: Related Workmentioning

confidence: 99%

A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer

Zhan

Long

et al. 2021

Sensors

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In many regions of the world, early diagnosis of non-small cell lung cancer (NSCLC) is a major challenge due to the large population and lack of medical resources, which is difficult toeffectively address via limited physician manpower alone. Therefore, we developed a convolutional neural network (CNN)-based assisted diagnosis and decision-making intelligent medical system with sensors. This system analyzes NSCLC patients’ medical records using sensors to assist staging a diagnosis and provides recommended treatment plans to physicians. To address the problem of unbalanced case samples across pathological stages, we used transfer learning and dynamic sampling techniques to reconstruct and iteratively train the model to improve the accuracy of the prediction system. In this paper, all data for training and testing the system were obtained from the medical records of 2,789,675 patients with NSCLC, which were recorded in three hospitals in China over a five-year period. When the number of case samples reached 8000, the system achieved an accuracy rate of 0.84, which is already close to that of the doctors (accuracy: 0.86). The experimental results proved that the system can quickly and accurately analyze patient data and provide decision information support for physicians.

“…CAD provides physicians with effective information to support disease treatment decisions, which to a certain extent alleviates the difficulties in early diagnosis of osteosarcoma caused by the shortage of specialized doctors in developing countries, and avoids misdiagnosis due to the time and effort spent by doctors in dealing with the complex diagnostic process and analyzing large amounts of case data. In recent years, machine learning has emerged and convolutional neural networks are widely used in image segmentation [ 11 ], and many full convolutional network (FCN)- [ 12 ] based methods have been proposed to accurately segment medical images. It can also be combined with squeeze-and-excitation (SE) block and multidataset training to improve the overall generalization ability of the model [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Rethinking U-Net from an Attention Perspective with Transformers for Osteosarcoma MRI Image Segmentation

Ouyang

Yang

Computational Intelligence and Neuroscience

et al. 2022

Self Cite

Osteosarcoma is one of the most common primary malignancies of bone in the pediatric and adolescent populations. The morphology and size of osteosarcoma MRI images often show great variability and randomness with different patients. In developing countries, with large populations and lack of medical resources, it is difficult to effectively address the difficulties of early diagnosis of osteosarcoma with limited physician manpower alone. In addition, with the proposal of precision medicine, existing MRI image segmentation models for osteosarcoma face the challenges of insufficient segmentation accuracy and high resource consumption. Inspired by transformer’s self-attention mechanism, this paper proposes a lightweight osteosarcoma image segmentation architecture, UATransNet, by adding a multilevel guided self-aware attention module (MGAM) to the encoder-decoder architecture of U-Net. We successively perform dataset classification optimization and remove MRI image irrelevant background. Then, UATransNet is designed with transformer self-attention component (TSAC) and global context aggregation component (GCAC) at the bottom of the encoder-decoder architecture to perform integration of local features and global dependencies and aggregation of contexts to learned features. In addition, we apply dense residual learning to the convolution module and combined with multiscale jump connections, to improve the feature extraction capability. In this paper, we experimentally evaluate more than 80,000 osteosarcoma MRI images and show that our UATransNet yields more accurate segmentation performance. The IOU and DSC values of osteosarcoma are 0.922 ± 0.03 and 0.921 ± 0.04, respectively, and provide intuitive and accurate efficient decision information support for physicians.