Analysis on Computer-Aided Diagnosis of Breast Tumors Based on GA-SVM

In order to improve the accuracy of breast tumor recognition, this paper proposes a breast tumor aided diagnosis analysis model. The method combines support vector machine (SVM) and genetic algorithms (GA). Firstly, GA is used to reduce the dimension of feature data obtained from breast tumor and the grid search method based on k fold cross validation error is used to select the model parameters of SVM. Following, the classification and recognition of breast tumors are implemented by the proposed method. In comparison with the results of LVQ, PSO-SVM and BP neural network breast tumor diagnosis methods, recognition accuracy of GA-SVM based method is more better than the traditional classification methods. As a result, it can provide decision support and assist doctors to minimize and avoid missing and faulty diagnostic cases when fine needle aspiration cytopathology is used, which is the traditional examination method, to diagnose breast tumors.