Automatic actomyosin complex selection using SVM
نویسندگان
چکیده
This paper describes that actomyosin complex particles are automatically selected. We propose a new approach, which combines both gray level co-occurrence matrix to extract texture features and SVM classifier to detect actomyosin complex particles automatically. Experimental results show that detection rate achieves 93.58%, the false positive rate is 3.66%, and the area under the ROC curve (AUC) is 0.9645.
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تاریخ انتشار 2005