基于多源特征智能融合技术在乳腺癌识别中的应用OACSTPCD

Objective To propose a multi-source features fusion technology combined with limited-memory broyden-fletcher-goldfarb-shanno-back propagation(L-BFGS-BP)neural network model to provide reference for screening and diagnosis of breast cancer.Methods A total of 388 breast cancer patients and 288 non breast cancer patients who were diagnosed in Jinling Hospital,Affiliated Hospital of Medical School,Nanjing University,from September 1,2016 to August 31,2022 were collected as research objects.Multi source feature sets were collected and sorted out from the aspects of biogenetics,clinical characteristics,serum markers,imaging,etc.The L-BFGS optimization algorithm and L-BFGS-BP model were established.Results Compared with random forest,BP neural network model,support vector machine,naive Bayes model,the accuracy of L-BFGS-BP model test increased by 8.07%,13.55%,3.55%and 8.39%,with statistically significant differences(P<0.05);the accuracy had been improved by 9.12%,16.42%,7.50%,and 7.19%respectively,with statistically significant differences(P<0.05).The L-BFGS-BP model also showed the same results in recall rate and F1 score.Conclusion L-BFGS-BP model has a better robustness,faster rate of convergence,better optimization ability,strong prediction ability,which has broad application prospects and research value.