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find Keyword "robustness" 2 results
  • A robust classification method for five types of leukocytes in peripheral blood based on mean-shift clustering

    A new leukocyte classification method for recognition of five types of human peripheral blood smear based on mean-shift clustering is proposed. The key idea of the proposed method is to extract the texture features of leukocytes in a visual manner which can benefit from human eyes. Firstly, some feature points are extracted in a gray leukocyte image by mean-shift. Secondly, these feature points are used as seeds of the region growing to expand feature regions which can express texture in visual mode to a certain extent. Finally, a parameter vector of these regions is extracted as the texture feature. Combing the vector with the geometric features of the leukocyte, the five typical classes of leukocytes can be recognized successfully using artificial neural network (ANN). A total number of 1 310 leukocyte images have been tested and the accurate rate of recognition for neutrophil, eosinophil, basophil, lymphocyte and monocyte are 95.4%, 93.8%, 100%, 93.1% and 92.4%, respectively, which shows the feasibility and high robustness of the proposed method.

    Release date:2018-10-19 03:21 Export PDF Favorites Scan
  • Robustness assessment of cardiovascular meta-analysis

    Objective To evaluate the robustness of cardiovascular meta-analysis with use of fragility index. Methods By searching PubMed, EMbase, and Web of Science databases from 2018 to 2022, relevant literature on cardiovascular meta-analysis was systematically collected and the fragility indexes were calculated; Spearman correlation analysis was used to explore the relationship between fragility index and sample size, total number of events, effect size and its confidence interval width. Results A total of 212 meta-analyses from 29 articles were included, with a median fragility index of 11 (5, 25), a median sample size of 10301 (3384, 48330), and a median total number of events of 360 (129, 1309). Most meta-analyses chose relative risk as the effect measure (179/212), and chose Mantel-Haenszel method (102/212) and random effects model (153/212). The fragility index was positively correlated with the sample size (rs=0.56, P<0.05) and the total number of events (rs=0.61, P<0.05), and negatively correlated with confidence interval width of the effect size (rs=−0.52, P<0.05). No statistically significant results were obtained in the correlation between the fragility index and effect size. Conclusion The fragility indexes of cardiovascular meta-analyses published in comprehensive journals of high impact factors and professional cardiovascular journals are generally low, and therefore lack robustness. Fragility index is suggested to be reported in medical researches, assisting in explaining the P-value.

    Release date:2024-01-23 06:09 Export PDF Favorites Scan
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