Abstract: A method for classifying multi-granularity breast cancer genes based on a double self-adaptive neighborhood radius includes large-scale gene locus data are read and normalized, and a data analysis is performed on the large-scale gene loci. An optimum value K is selected by adopting a combination of contour coefficients and a PCA dimensionality reduction visualization, and a model of information granulation is adjusted. A heuristic reduction algorithm is used to implement a multi-granularity attribute reduction of a self-adaptive neighborhood radius based on a cluster center distance and a multi-granularity attribute reduction of a neighborhood radius based on an attribute inclusion degree, and big data for breast cancer genes are classified and predicted by adopting a machine learning classification algorithm based on a SVM support vector machine.

Abstract: A method for classifying multi-granularity breast cancer genes based on a double self-adaptive neighborhood radius includes large-scale gene locus data are read and normalized, and a data analysis is performed on the large-scale gene loci. An optimum value K is selected by adopting a combination of contour coefficients and a PCA dimensionality reduction visualization, and a model of information granulation is adjusted. A heuristic reduction algorithm is used to implement a multi-granularity attribute reduction of a self-adaptive neighborhood radius based on a cluster center distance and a multi-granularity attribute reduction of a neighborhood radius based on an attribute inclusion degree, and big data for breast cancer genes are classified and predicted by adopting a machine learning classification algorithm based on a SVM support vector machine.

Abstract: Disclosed is a fully convolutional genetic neural network method for segmentation of infant brain record images. First, infant brain record image data is input and preprocessed, and genetic coding initialization is performed for parameters according to the length of a DMPGA-FCN network weight. Then, m individuals are randomly grouped into genetic native subpopulations and corresponding twin subpopulations are derived, where respective crossover probability and mutation probability pm of all the subpopulations are determined from disjoint intervals; and an optimal initialization value fa is searched for by using a genetic operator. Afterwards, fa is used as a forward propagation calculation parameter and a weighting operation is performed on the feature address featuremap.

Abstract: Disclosed is a fully convolutional genetic neural network method for segmentation of infant brain record images. First, infant brain record image data is input and preprocessed, and genetic coding initialization is performed for parameters according to the length of a DMPGA-FCN network weight. Then, m individuals are randomly grouped into genetic native subpopulations and corresponding twin subpopulations are derived, where respective crossover probability and mutation probability pm of all the subpopulations are determined from disjoint intervals; and an optimal initialization value fa is searched for by using a genetic operator. Afterwards, fa is used as a forward propagation calculation parameter and a weighting operation is performed on the feature address featuremap.