Genetic-Based Nuclear Body SVM Training Data Before Cutting

We focused on the body three-dimensional medical image data prior to cutting effective training method. We focus on the methods which is the body three-dimensional medical image data prior to cutting effective training. Digital images based on various methods of training, the support vector machine based on image data before cutting training algorithms. Though the various methods of the training, which is the digital images, we have a method based on image data before cutting traini algorithms. The main study is how to find the principle of analysis which is difficult to find from a number of observational datas, we can use these laws to analyze the objective targets, and forecast the data which is in the future or is not observed. Its proposed solute the use of image features pre-cut image of the key issues of training data directly. In the analysis based on support vector machine training advantages and disadvantages of image data, based on support vector machines through is able to describe the distribution characteristics of data sets to analyze the nature of the proposed genetic-based pattern recognition for the support vector machine nuclear. Through the training of medical image data of the experiment shows that the genetic-based nuclear support vector machine significantly better in the performance of support vector machine in the basic model and prove that the method can be cut to achieve satisfactory results.