LVQ神经网络的分类_乳腺肿瘤诊断_matlab

%% LVQ神经网络的分类——乳腺肿瘤诊断 %% 清空环境变量 clear all clc warning off %% 导入数据 load data.mat a=randperm(569); Train=data(a(1:500),:); Test=data(a(501:end),:); % 训练数据 P_train=Train(:,3:end)'; Tc_train=Train(:,2)'; T_train=ind2vec(Tc_train); % 测试数据 P_test=Test(:,3:end)'; Tc_test=Test(:,2)'; %% K-fold交叉验证确定最佳神经元个数 k_fold=5; Indices=crossvalind('Kfold',size(P_train,2),k_fold); error_min=10e10; best_number=1; best_input=[]; best_output=[]; best_train_set_index=[]; best_validation_set_index=[]; h=waitbar(0,'正在寻找最佳神经元个数.....'); for i=1:k_fold % 验证集标号 validation_set_index=(Indices==i); % 训练集标号 train_set_index=~validation_set_index; % 验证集 validation_set_input=P_train(:,validation_set_index); validation_set_output=T_train(:,validation_set_index); % 训练集 train_set_input=P_train(:,train_set_index); train_set_output=T_train(:,train_set_index); for number=10:30 count_B_train=length(find(Tc_train(:,train_set_index)==1)); count_M_train=length(find(Tc_train(:,train_set_index)==2)); rate_B=count_B_train/length(find(train_set_index==1)); rate_M=count_M_train/length(find(train_set_index==1)); net=newlvq(minmax(train_set_input),number,[rate_B rate_M]); % 设置网络参数 net.trainParam.epochs=1000; net.trainParam.show=10; net.trainParam.lr=0.1; net.trainParam.goal=0.1; % 训练网络 net=train(net,train_set_input,train_set_output); waitbar(((i-1)*21+number)/114,h); %% 仿真测试 T_sim=sim(net,validation_set_input); Tc_sim=vec2ind(T_sim); error=length(find(Tc_sim~=Tc_train(:,validation_set_index))); if error<error_min error_min=error; best_number=number; best_input=train_set_input; best_output=train_set_output; best_train_set_index=train_set_index; best_validation_set_index=validation_set_index; end end end disp(['经过交叉验证，得到的最佳神经元个数为：' num2str(best_number)]); close(h); %% 创建网络 count_B_train=length(find(Tc_train(:,best_train_set_index)==1)); count_M_train=length(find(Tc_train(:,best_train_set_index)==2)); rate_B=count_B_train/length(find(train_set_index==1)); rate_M=count_M_train/length(find(train_set_index==1)); net=newlvq(minmax(best_input),best_number,[rate_B rate_M]); % 设置网络参数 net.trainParam.epochs=1000; net.trainParam.show=10; net.trainParam.lr=0.1; net.trainParam.goal=0.1; %% 训练网络 net=train(net,best_input,best_output); %% 仿真测试 T_sim=sim(net,P_test); Tc_sim=vec2ind(T_sim); result=[Tc_sim;Tc_test] %% 结果显示 total_B=length(find(data(:,2)==1)); total_M=length(find(data(:,2)==2)); count_B_validation=length(find(Tc_train(:,best_validation_set_index)==1)); count_M_validation=length(find(Tc_train(:,best_validation_set_index)==2)); number_B=length(find(Tc_test==1)); number_M=length(find(Tc_test==2)); number_B_sim=length(find(Tc_sim==1 & Tc_test==1)); number_M_sim=length(find(Tc_sim==2 &Tc_test==2)); disp(['病例总数：' num2str(569)... ' 良性：' num2str(total_B)... ' 恶性：' num2str(total_M)]); disp(['训练集病例总数：' num2str(length(find(best_train_set_index==1)))... ' 良性：' num2str(count_B_train)... ' 恶性：' num2str(count_M_train)]); disp(['验证集病例总数：' num2str(length(find(best_validation_set_index==1)))... ' 良性：' num2str(count_B_validation)... ' 恶性：' num2str(count_M_validation)]); disp(['测试集病例总数：' num2str(69)... ' 良性：' num2str(number_B)... ' 恶性：' num2str(number_M)]); disp(['良性乳腺肿瘤确诊：' num2str(number_B_sim)... ' 误诊：' num2str(number_B-number_B_sim)... ' 确诊率p1=' num2str(number_B_sim/number_B*100) '%']); disp(['恶性乳腺肿瘤确诊：' num2str(number_M_sim)... ' 误诊：' num2str(number_M-number_M_sim)... ' 确诊率p2=' num2str(number_M_sim/number_M*100) '%']);