【免费】LVQ神经网络的分类——乳腺肿瘤诊断.rar资源-CSDN文库

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LVQ神经网络的分类——乳腺肿瘤诊断.rar （4个子文件）

案例21 LVQ神经网络的分类——乳腺肿瘤诊断

crossvalidation_lvq.m 6KB

chapter21_lvq.m 4KB

chapter21_bp.m 3KB

data.mat 84KB

%% LVQ神经网络的分类——乳腺肿瘤诊断 % % <html> % <table border="0" width="600px" id="table1"> <tr> <td>该案例作者申明：</td> </tr> <tr> <td>1：本人长期驻扎在此<a target="_blank" href="http://www.ilovematlab.cn/forum-158-1.html">板块</a>里，对<a target="_blank" href="http://www.ilovematlab.cn/thread-49221-1-1.html">该案例</a>提问，做到有问必答。</td></tr><tr> <td>2：此案例有配套的教学视频，配套的完整可运行Matlab程序。</td> </tr> <tr> <td> 3：以下内容为该案例的部分内容（约占该案例完整内容的1/10）。</td> </tr> <tr> <td> 4：此案例为原创案例，转载请注明出处（<a target="_blank" href="http://www.ilovematlab.cn/">Matlab中文论坛</a>，<a target="_blank" href="http://www.ilovematlab.cn/forum-158-1.html">《Matlab神经网络30个案例分析》</a>）。</td> </tr> <tr> <td> 5：若此案例碰巧与您的研究有关联，我们欢迎您提意见，要求等，我们考虑后可以加在案例里。</td> </tr> <tr> <td> 6：您看到的以下内容为初稿，书籍的实际内容可能有少许出入，以书籍实际发行内容为准。</td> </tr><tr> <td> 7：此书其他常见问题、预定方式等，<a target="_blank" href="http://www.ilovematlab.cn/thread-47939-1-1.html">请点击这里</a>。</td> </tr></table> % </html> % web browser http://www.ilovematlab.cn/thread-61656-1-1.html %% 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) '%']); web browser http://www.ilovematlab.cn/thread-61656-1-1.html %% % % <html> % <table align="center" > <tr> <td align="center">版权所有：<a % href="http://www.ilovematlab.cn/">Matlab中文论坛</a>   <script % src="http://s3.cnzz.com/stat.php?id=971931&web_id=971931&show=pic" language="JavaScript" ></script> </td> </tr></table> % </html> %

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