RETRACTED ARTICLE: An improved approach for automatic spine canal segmentation using probabilistic boosting tree (PBT) with fuzzy support vector machine

Viji, C.; Rajkumar, N.; Suganthi, S.; Venkatachalam, K.; Kumar, Tarun; Pandiyan, Sanjeevi

doi:10.1007/s12652-020-02267-6

Cited by 9 publications

(2 citation statements)

References 25 publications

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“…The second type is the automatic segmentation or grading research directly based on the intervertebral foramen. Reference [ 11 ] realized the segmentation by detecting the center line of the vertebral body and the main boundary points of the intervertebral foramen; [ 12 ] used the AdaBoost detection algorithm and iterative normalization segmentation algorithm to detect MRI vertebral body images and locate and segment them at the same time; [ 13 ] realized the modeling and segmentation of spine and intervertebral foramen based on two scales by representing the global spine shape in a continuous local vertebral coordinate system and modeling the individual vertebrae as a triangular surface network; [ 14 ] used the super-pixel segmentation method to achieve an accuracy of 98.52% in the image location and benign and malignant classification of intervertebral foramen stenosis.…”

Section: Introductionmentioning

confidence: 99%

Study on Automatic Multi-Classification of Spine Based on Deep Learning and Postoperative Infection Screening

Wang

Liu

2022

Journal of Healthcare Engineering

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The preoperative qualitative and hierarchical diagnosis of intervertebral foramen stenosis is very important for clinicians to explore the effect of multimodal analgesia nursing on pain control after spinal fusion and to formulate treatment strategies and patients’ health recovery. However, there are still many problems in this aspect, and there is a lack of relevant research and effective methods to assist clinicians in diagnosis. Therefore, to improve the accuracy of computer-aided diagnosis of intervertebral foramen stenosis and the work efficiency of doctors, a deep learning automatic grading algorithm of intervertebral foramen stenosis image is proposed in this study. The image of intervertebral foramen was extracted from the MRI image of sagittal spine, and the image was preprocessed. 86 patients with spinal fusion treated in our hospital, specifically from May 2018 to May 2020, were randomly divided into the control group (routine analgesic nursing) and the multimodal group (multimodal analgesic nursing), with 43 cases in each group. The pain control effect and satisfaction of the two groups were observed. The results after multimodal analgesia nursing show that the VASs of the multimodal group at different time points were significantly lower than those of the control group P < 0.05 ; the satisfaction score of pain control in the multimodal group was significantly higher than that in the control group P < 0.05 . Multimodal analgesia nursing for patients undergoing spinal fusion can effectively reduce the degree of postoperative pain and improve the effect of pain control and satisfaction with pain control, which is worthy of promotion.

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Section: Introductionmentioning

confidence: 99%

Study on Automatic Multi-Classification of Spine Based on Deep Learning and Postoperative Infection Screening

Wang

Liu

2022

Journal of Healthcare Engineering

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“…The performance of the proposed model is compared against the various other approaches concerning the parameters like Accuracy, Sensitivity, and Specificity of each of the approaches like Decision Tree and Random Forest approaches, Lesion Index Calculation Unit (LICU) approach, Fuzzy Support Vector Machine with probabilistic boosting the segmentation, Compact Deep Neural Network, SegNet model, U-Net model, respectively [ 81 , 82 , 83 , 84 , 85 ], considered for comparative analysis that determine the efficiency of the model. Figure 10 is the graph that is obtained from the values of Table 4 .…”

Section: Resultsmentioning

confidence: 99%

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Srinivasu

SivaSai

Ijaz

et al. 2021

Sensors

404

169

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Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

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A Two-Stage Network for Segmentation of Vertebrae and Intervertebral Discs: Integration of Efficient Local-Global Fusion Using 3D Transformer and 2D CNN

Li,

Zhou,

Tong

2023

Communications in Computer and Information Science

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RETRACTED ARTICLE: An improved approach for automatic spine canal segmentation using probabilistic boosting tree (PBT) with fuzzy support vector machine

Cited by 9 publications

References 25 publications

Study on Automatic Multi-Classification of Spine Based on Deep Learning and Postoperative Infection Screening

Study on Automatic Multi-Classification of Spine Based on Deep Learning and Postoperative Infection Screening

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

A Two-Stage Network for Segmentation of Vertebrae and Intervertebral Discs: Integration of Efficient Local-Global Fusion Using 3D Transformer and 2D CNN

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