An Effective Nuclear Extraction Mask Method for SVM Classification

With the development of medical technology, the automatic cell analysis system plays an important role in medical diagnosis and medical image processing. The kernel recognition theory and technology based on support vector machine (SVM) classifier are mainly optimized from the perspective of the kernel segmentation algorithm to improve the recognition accuracy of the SVM classifier. Unfortunately, the nuclear overlap treatment can not accurately separate the nuclear gelling impurities in the dyeing process, resulting in the low classification accuracy of SVM. To solve the above image segmentation problems in the process of nuclear imaging processing, an effective nuclear extraction method based on the mask method for the SVM classifier is proposed. Compared with related work, the proposed method enables one to achieve a higher accuracy of SVM cross-validation.