scholarly journals SK-FMYOLOV3: A Novel Detection Method for Urine Test Strips

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Rui Yang ◽  
Yonglin Zhang ◽  
Zhenrong Deng ◽  
Wenming Huang ◽  
Rushi Lan ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Test Strip ◽  
Recall Rate ◽  
Detection Algorithm ◽  
Urine Test ◽  
Feature Maps ◽  
Test Strips ◽  
Fuzzy C Means Clustering ◽  
Convolution Kernels ◽  
Clustering Center

To accurately detect small defects in urine test strips, the SK-FMYOLOV3 defect detection algorithm is proposed. First, the prediction box clustering algorithm of YOLOV3 is improved. The fuzzy C-means clustering algorithm is used to generate the initial clustering centers, and then, the clustering center is passed to the K-means algorithm to cluster the prediction boxes. To better detect smaller defects, the YOLOV3 feature map fusion is increased from the original three-scale prediction to a four-scale prediction. At the same time, 23 convolutional layers of size 3 × 3 in the YOLOV3 network are replaced with SkNet structures, so that different feature maps can independently select different convolution kernels for training, improving the accuracy of defect classification. We collected and enhanced urine test strip images in industrial production and labeled the small defects in the images. A total of 11634 image sets were used for training and testing. The experimental results show that the algorithm can obtain an anchor frame with an average cross ratio of 86.57, while the accuracy rate and recall rate of nonconforming products are 96.8 and 94.5, respectively. The algorithm can also accurately identify the category of defects in nonconforming products.

Quantitative measurement of ketone bodies in urine using reflectometry

2005 ◽  
Vol 43 (7) ◽  
Author(s):  
Joris Penders ◽  
Tom Fiers ◽  
Mimi Giri ◽  
Birgitte Wuyts ◽  
Larissa Ysewyn ◽  
...  
Keyword(s):  
Quantitative Measurement ◽  
Ketone Bodies ◽  
Limit Of Detection ◽  
Test Strip ◽  
Urine Test ◽  
Fast Analysis ◽  
Test Strips ◽  
Pre Treatment ◽  
Body Production ◽  
Reflectance Data

AbstractBackground: Recently, automated urine test strip readers became available that can report quantitative data. We explored the possibility of measuring all ketone bodies (acetone, acetoacetate, 3-hydroxybutyrate) in urine with these test strips. Monitoring urinary ketone concentrations could offer the advantages of measuring higher values (due to the low renal thresholds) and being less sensitive to fluctuations.Methods: We evaluated URISYS 2400 (Roche) quantitative reflectance data for the ketone reflectance field and compared it with biochemical data from urine samples. Using an easy sample pre-treatment with 3-hydroxybutyrate dehydrogenase, we were able to assay 3-hydroxybutyrate as well, which normally does not react on urine test strips.Results: Within- and between-run reproducibility of the reflectance signal for high- and low-concentration urine pools was 11.0–3.6% and 11.0–5.8% for aceto-acetate, 8.2–9.2% and 10.4–16.1% for acetone, and 5.1–3.0% and 5.6–3.5% for 3-hydroxybutyrate, respectively. The lower limit of detection for acetoacetate was 0.13mmol/L (CV=3.6%). Fair agreement was obtained between test strip data for ketones andcolorimetrically determined acetoacetate values (r=0.90).Conclusions: In urine test strip analysis, quantitative ketone reflectance data allow a simple and fast analysis, offering affordable screening for the detection of ketone body production in diabetes, especially in emergency settings.

scholarly journals Community Detection Based on Graph Representation Learning in Evolutionary Networks

2021 ◽  
Vol 11 (10) ◽  
pp. 4497
Author(s):  
Dongming Chen ◽  
Mingshuo Nie ◽  
Jie Wang ◽  
Yun Kong ◽  
Dongqi Wang ◽  
...  
Keyword(s):  
Community Detection ◽  
Network Structure ◽  
Clustering Algorithm ◽  
Laplacian Matrix ◽  
Representation Learning ◽  
Detection Algorithm ◽  
Graph Representation ◽  
Time Slice ◽  
Current Time ◽  
Evolutionary Networks

Aiming at analyzing the temporal structures in evolutionary networks, we propose a community detection algorithm based on graph representation learning. The proposed algorithm employs a Laplacian matrix to obtain the node relationship information of the directly connected edges of the network structure at the previous time slice, the deep sparse autoencoder learns to represent the network structure under the current time slice, and the K-means clustering algorithm is used to partition the low-dimensional feature matrix of the network structure under the current time slice into communities. Experiments on three real datasets show that the proposed algorithm outperformed the baselines regarding effectiveness and feasibility.

Segmentation of corpus callosum based on tensor fuzzy clustering algorithm

2021 ◽  
pp. 1-14
Author(s):  
Yujia Qu ◽  
Yuanjun Wang
Keyword(s):  
Corpus Callosum ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Diffusion Tensor ◽  
Region Of Interest ◽  
Midsagittal Plane ◽  
Fuzzy Clustering Algorithm ◽  
Human Connectome Project ◽  
Clustering Center ◽  
Fixed Region

BACKGROUND: The corpus callosum in the midsagittal plane plays a crucial role in the early diagnosis of diseases. When the anisotropy of the diffusion tensor in the midsagittal plane is calculated, the anisotropy of corpus callosum is close to that of the fornix, which leads to blurred boundary of the segmentation region. OBJECTIVE: To apply a fuzzy clustering algorithm combined with new spatial information to achieve accurate segmentation of the corpus callosum in the midsagittal plane in diffusion tensor images. METHODS: In this algorithm, a fixed region of interest is selected from the midsagittal plane, and the anisotropic filtering algorithm based on tensor is implemented by replacing the gradient direction of the structural tensor with an eigenvector, thus filtering the diffusion tensor of region of interest. Then, the iterative clustering center based on K-means clustering is used as the initial clustering center of tensor fuzzy clustering algorithm. Taking filtered diffusion tensor as input data and different metrics as similarity measures, the neighborhood diffusion tensor pixel calculation method of Log Euclidean framework is introduced in the membership function calculation, and tensor fuzzy clustering algorithm is proposed. In this study, MGH35 data from the Human Connectome Project (HCP) are tested and the variance, accuracy and specificity of the experimental results are discussed. RESULTS: Segmentation results of three groups of subjects in MGH35 data are reported. The average segmentation accuracy is 97.34%, and the average specificity is 98.43%. CONCLUSIONS: When segmenting the corpus callosum of diffusion tensor imaging, our method cannot only effective denoise images, but also achieve high accuracy and specificity.

scholarly journals Automatic measurement of traditional Chinese costume from its silhouette through Fuzzy c-means clustering method

2020 ◽  
Vol 15 ◽  
pp. 155892502097832
Author(s):  
Jiaqin Zhang ◽  
Jingan Wang ◽  
Le Xing ◽  
Hui’e Liang
Keyword(s):  
Industrial Application ◽  
Clustering Algorithm ◽  
Color Space ◽  
Automatic Measurement ◽  
Feature Point ◽  
Feature Points ◽  
Point Location ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Environmental Robustness

As the precious cultural heritage of the Chinese nation, traditional costumes are in urgent need of scientific research and protection. In particular, there are scanty studies on costume silhouettes, due to the reasons of the need for cultural relic protection, and the strong subjectivity of manual measurement, which limit the accuracy of quantitative research. This paper presents an automatic measurement method for traditional Chinese costume dimensions based on fuzzy C-means clustering and silhouette feature point location. The method is consisted of six steps: (1) costume image acquisition; (2) costume image preprocessing; (3) color space transformation; (4) object clustering segmentation; (5) costume silhouette feature point location; and (6) costume measurement. First, the relative total variation model was used to obtain the environmental robustness and costume color adaptability. Second, the FCM clustering algorithm was used to implement image segmentation to extract the outer silhouette of the costume. Finally, automatic measurement of costume silhouette was achieved by locating its feature points. The experimental results demonstrated that the proposed method could effectively segment the outer silhouette of a costume image and locate the feature points of the silhouette. The measurement accuracy could meet the requirements of industrial application, thus providing the dual value of costume culture research and industrial application.

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